{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# chp02-Introduction(2013)\n",
    "\n",
    ">因为我在做第三章的笔记时，才发现作者已经在2017推出了第二版，但难得写了这么多，就留下来了，所以这里的chp02-Introduction(2013)指的是2013年第一版的第二章，这一章的内容主要是直接使用pandas对一些数据集进行数据分析，可以说是实战篇。不过在2017年的第二版中，这一章的内容被移到了第十四章，内容为14.1，14.2，14.3。第十四章中把不同的数据集分成了不同的小节，阅读起来会更方便一些，而且有很多地方做了优化，推荐大家直接看第十四章的内容。\n",
    "\n",
    "# 读取来自bit.ly的1.use.gov数据\n",
    "\n",
    "这个数据集是bit.ly和美国政府网站use.gov合作，提供了一份从生成.gov或.mil短链接的用户那里手机来的匿名数据。\n",
    "\n",
    "下面文中的各行格式为JSON，这里读取第一行看一看是什么东西"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = '../datasets/bitly_usagov/example.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{ \"a\": \"Mozilla\\\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\\\/535.11 (KHTML, like Gecko) Chrome\\\\/17.0.963.78 Safari\\\\/535.11\", \"c\": \"US\", \"nk\": 1, \"tz\": \"America\\\\/New_York\", \"gr\": \"MA\", \"g\": \"A6qOVH\", \"h\": \"wfLQtf\", \"l\": \"orofrog\", \"al\": \"en-US,en;q=0.8\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\\\/\\\\/www.facebook.com\\\\/l\\\\/7AQEFzjSi\\\\/1.usa.gov\\\\/wfLQtf\", \"u\": \"http:\\\\/\\\\/www.ncbi.nlm.nih.gov\\\\/pubmed\\\\/22415991\", \"t\": 1331923247, \"hc\": 1331822918, \"cy\": \"Danvers\", \"ll\": [ 42.576698, -70.954903 ] }\\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(path).readline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "python有很多内置的模块能把JSON字符串转换成Python字典对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "path = '../datasets/bitly_usagov/example.txt'\n",
    "records = [json.loads(line) for line in open(path)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面这种方法叫做列表推导式, list comprehension, 在一组字符串上执行一条相同操作（比如这里的json.loads）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.78 Safari/535.11',\n",
       " 'al': 'en-US,en;q=0.8',\n",
       " 'c': 'US',\n",
       " 'cy': 'Danvers',\n",
       " 'g': 'A6qOVH',\n",
       " 'gr': 'MA',\n",
       " 'h': 'wfLQtf',\n",
       " 'hc': 1331822918,\n",
       " 'hh': '1.usa.gov',\n",
       " 'l': 'orofrog',\n",
       " 'll': [42.576698, -70.954903],\n",
       " 'nk': 1,\n",
       " 'r': 'http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/wfLQtf',\n",
       " 't': 1331923247,\n",
       " 'tz': 'America/New_York',\n",
       " 'u': 'http://www.ncbi.nlm.nih.gov/pubmed/22415991'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'America/New_York'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records[0]['tz']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用纯python代码对时区进行计数\n",
    "\n",
    "我们想知道数据集中出现在哪个时区（即tz字段）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'tz'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-db4fbd348da9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtime_zones\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mrec\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tz'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrec\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-10-db4fbd348da9>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtime_zones\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mrec\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tz'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrec\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 'tz'"
     ]
    }
   ],
   "source": [
    "time_zones = [rec['tz'] for rec in records]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看来并不是所有的记录都有时区字段。那么只需要在推导式的末尾加一个if 'tz' in rec判断即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "time_zones = [rec['tz'] for rec in records if 'tz' in rec]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['America/New_York',\n",
       " 'America/Denver',\n",
       " 'America/New_York',\n",
       " 'America/Sao_Paulo',\n",
       " 'America/New_York',\n",
       " 'America/New_York',\n",
       " 'Europe/Warsaw',\n",
       " '',\n",
       " '',\n",
       " '']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_zones[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了对时区进行计数，我们用两种方法：一个用python，比较麻烦。另一个用pandas，比较简单。 \n",
    "\n",
    "这里我们遍历时区的过程中将计数值保存在字典中："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_counts(sequence):\n",
    "    counts = {}\n",
    "    for x in sequence:\n",
    "        if x in counts:\n",
    "            counts[x] += 1\n",
    "        else:\n",
    "            counts[x] = 1\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用python标准库的话，能把代码写得更简洁一些："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def get_counts2(sequence):\n",
    "    counts = defaultdict(int) # 所有的值均会被初始化为0\n",
    "    for x in sequence:\n",
    "        counts[x] += 1\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（译者：下面关于defaultdict的用法是我从Stack Overflow上找到的，英文比较多，简单的说就是通常如果一个字典里不存在一个key，调用的时候会报错，但是如果我们设置了了default，就不会被报错，而是会新建一个key，对应的value就是我们设置的int，这里int代表0）\n",
    "\n",
    "**defaultdict** means that if a key is not found in the dictionary, then instead of a KeyError being thrown, a new entry is created. The type of this new entry is given by the argument of defaultdict.\n",
    "```\n",
    "somedict = {}\n",
    "print(somedict[3]) # KeyError\n",
    "\n",
    "someddict = defaultdict(int)\n",
    "print(someddict[3]) # print int(), thus 0\n",
    "```\n",
    "\n",
    "Usually, a Python dictionary throws a KeyError if you try to get an item with a key that is not currently in the dictionary. The defaultdict in contrast will simply create any items that you try to access (provided of course they do not exist yet). To create such a \"default\" item, it calls the function object that you pass in the constructor (more precisely, it's an arbitrary \"callable\" object, which includes function and type objects). For the first example, default items are created using `int()`, which will return the integer object 0. For the second example, default items are created using `list()`, which returns a new empty list object.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "someddict = defaultdict(int)\n",
    "print(someddict[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "someddict[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面用函数的方式写出来是为了有更高的可用性。要对它进行时区处理，只需要将time_zones传入即可："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "counts = get_counts(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1251"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts['America/New_York']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3440"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(time_zones)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如何想要得到前10位的时区及其计数值，我们需要一些有关字典的处理技巧："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def top_counts(count_dict, n=10):\n",
    "    value_key_pairs = [(count, tz) for tz, count in count_dict.items()]\n",
    "    value_key_pairs.sort()\n",
    "    return value_key_pairs[-n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(33, 'America/Sao_Paulo'),\n",
       " (35, 'Europe/Madrid'),\n",
       " (36, 'Pacific/Honolulu'),\n",
       " (37, 'Asia/Tokyo'),\n",
       " (74, 'Europe/London'),\n",
       " (191, 'America/Denver'),\n",
       " (382, 'America/Los_Angeles'),\n",
       " (400, 'America/Chicago'),\n",
       " (521, ''),\n",
       " (1251, 'America/New_York')]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_counts(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果用python标准库里的collections.Counter类，能让这个任务变得更简单"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counts = Counter(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('America/New_York', 1251),\n",
       " ('', 521),\n",
       " ('America/Chicago', 400),\n",
       " ('America/Los_Angeles', 382),\n",
       " ('America/Denver', 191),\n",
       " ('Europe/London', 74),\n",
       " ('Asia/Tokyo', 37),\n",
       " ('Pacific/Honolulu', 36),\n",
       " ('Europe/Madrid', 35),\n",
       " ('America/Sao_Paulo', 33)]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用pandas对时区进行计数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame是pandas中最重要的数据结构，它用于将数据表示变为一个表格。从一组原始记录中创建DataFrame是很简单的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import DataFrame, Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd; import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frame = DataFrame(records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_heartbeat_</th>\n",
       "      <th>a</th>\n",
       "      <th>al</th>\n",
       "      <th>c</th>\n",
       "      <th>cy</th>\n",
       "      <th>g</th>\n",
       "      <th>gr</th>\n",
       "      <th>h</th>\n",
       "      <th>hc</th>\n",
       "      <th>hh</th>\n",
       "      <th>kw</th>\n",
       "      <th>l</th>\n",
       "      <th>ll</th>\n",
       "      <th>nk</th>\n",
       "      <th>r</th>\n",
       "      <th>t</th>\n",
       "      <th>tz</th>\n",
       "      <th>u</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Danvers</td>\n",
       "      <td>A6qOVH</td>\n",
       "      <td>MA</td>\n",
       "      <td>wfLQtf</td>\n",
       "      <td>1.331823e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>orofrog</td>\n",
       "      <td>[42.576698, -70.954903]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.ncbi.nlm.nih.gov/pubmed/22415991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GoogleMaps/RochesterNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Provo</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>UT</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>1.308262e+09</td>\n",
       "      <td>j.mp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[40.218102, -111.613297]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.AwareMap.com/</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/Denver</td>\n",
       "      <td>http://www.monroecounty.gov/etc/911/rss.php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
       "      <td>en-US</td>\n",
       "      <td>US</td>\n",
       "      <td>Washington</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>DC</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>1.331920e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[38.9007, -77.043098]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://t.co/03elZC4Q</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://boxer.senate.gov/en/press/releases/0316...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n",
       "      <td>pt-br</td>\n",
       "      <td>BR</td>\n",
       "      <td>Braz</td>\n",
       "      <td>zCaLwp</td>\n",
       "      <td>27</td>\n",
       "      <td>zUtuOu</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alelex88</td>\n",
       "      <td>[-23.549999, -46.616699]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>http://apod.nasa.gov/apod/ap120312.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Shrewsbury</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>MA</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>1.273672e+09</td>\n",
       "      <td>bit.ly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[42.286499, -71.714699]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/selco/</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   _heartbeat_                                                  a  \\\n",
       "0          NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "1          NaN                             GoogleMaps/RochesterNY   \n",
       "2          NaN  Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...   \n",
       "3          NaN  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...   \n",
       "4          NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "\n",
       "               al   c          cy       g  gr       h            hc  \\\n",
       "0  en-US,en;q=0.8  US     Danvers  A6qOVH  MA  wfLQtf  1.331823e+09   \n",
       "1             NaN  US       Provo  mwszkS  UT  mwszkS  1.308262e+09   \n",
       "2           en-US  US  Washington  xxr3Qb  DC  xxr3Qb  1.331920e+09   \n",
       "3           pt-br  BR        Braz  zCaLwp  27  zUtuOu  1.331923e+09   \n",
       "4  en-US,en;q=0.8  US  Shrewsbury  9b6kNl  MA  9b6kNl  1.273672e+09   \n",
       "\n",
       "          hh   kw         l                        ll   nk  \\\n",
       "0  1.usa.gov  NaN   orofrog   [42.576698, -70.954903]  1.0   \n",
       "1       j.mp  NaN     bitly  [40.218102, -111.613297]  0.0   \n",
       "2  1.usa.gov  NaN     bitly     [38.9007, -77.043098]  1.0   \n",
       "3  1.usa.gov  NaN  alelex88  [-23.549999, -46.616699]  0.0   \n",
       "4     bit.ly  NaN     bitly   [42.286499, -71.714699]  0.0   \n",
       "\n",
       "                                                   r             t  \\\n",
       "0  http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...  1.331923e+09   \n",
       "1                           http://www.AwareMap.com/  1.331923e+09   \n",
       "2                               http://t.co/03elZC4Q  1.331923e+09   \n",
       "3                                             direct  1.331923e+09   \n",
       "4                http://www.shrewsbury-ma.gov/selco/  1.331923e+09   \n",
       "\n",
       "                  tz                                                  u  \n",
       "0   America/New_York        http://www.ncbi.nlm.nih.gov/pubmed/22415991  \n",
       "1     America/Denver        http://www.monroecounty.gov/etc/911/rss.php  \n",
       "2   America/New_York  http://boxer.senate.gov/en/press/releases/0316...  \n",
       "3  America/Sao_Paulo            http://apod.nasa.gov/apod/ap120312.html  \n",
       "4   America/New_York  http://www.shrewsbury-ma.gov/egov/gallery/1341...  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     America/New_York\n",
       "1       America/Denver\n",
       "2     America/New_York\n",
       "3    America/Sao_Paulo\n",
       "4     America/New_York\n",
       "5     America/New_York\n",
       "6        Europe/Warsaw\n",
       "7                     \n",
       "8                     \n",
       "9                     \n",
       "Name: tz, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['tz'][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里frame的输出形式是summary view, 主要用于较大的dataframe对象。frame['tz']所返回的Series对象有一个value_counts方法，该方法可以让我们得到所需要的信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tz_counts = frame['tz'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "America/New_York       1251\n",
       "                        521\n",
       "America/Chicago         400\n",
       "America/Los_Angeles     382\n",
       "America/Denver          191\n",
       "Europe/London            74\n",
       "Asia/Tokyo               37\n",
       "Pacific/Honolulu         36\n",
       "Europe/Madrid            35\n",
       "America/Sao_Paulo        33\n",
       "Name: tz, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们想利用matplotlib为这段数据生成一张图片。所以，我们先给记录中未知或缺失的时区填上一个替代值。fillna函数可以替代缺失值（NA），而未知值（空字符串）则可以通过布尔型数组索引，加以替换："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clean_tz = frame['tz'].fillna('Missing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clean_tz[clean_tz == ''] = 'Unknown'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tz_counts = clean_tz.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "America/New_York       1251\n",
       "Unknown                 521\n",
       "America/Chicago         400\n",
       "America/Los_Angeles     382\n",
       "America/Denver          191\n",
       "Missing                 120\n",
       "Europe/London            74\n",
       "Asia/Tokyo               37\n",
       "Pacific/Honolulu         36\n",
       "Europe/Madrid            35\n",
       "Name: tz, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用counts对象的plot方法即可得到一张水平条形图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1136a2c50>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XRXd3O3PmzHF4NDMzG0EOj3VC0prALGCziJhWwzqWB14FdomI60a+grWAMSN/WDMzMyvF\nC2aGgaStJL0u6cpB7DYbWB24fzGON0HS45IW9PN6Q9J2gx3bzMzMLM8zj8PjIOAM4CBJq0fEPwba\nISICeHoxj7cH8HXgtlzbGcA7gQMAZW3PLeb4ZmZmZoBnHoecpHcAewM/Ba4ihbeebStLmiLpaUmv\nSJoh6XPZtjWzGcJNss/LSDpH0iNZ3wclHVbleP8ObABcGxFP97xIt57nR8QzufbXs30Oy8adL+kB\nSf81wDmdLGm2pPUlfU/SX6v06ZL0zVztEyU9Ialb0l2SPrZYF9TMzMzqisPj0Nsb6IqIh4EppFnI\nHt8F1gd2zv49GJiT2x6598sAjwOfAlqAE4DvSfp04Xh7ALdExEtlipPUBpyc1bIhcAFwkaT/qNJX\nkn4B7AlsExEPAucCm0vaMNfvw8A6wP9mTd/Izu0QYBPgVuAqSV7ZYmZm1uAcHofegcCF2ftrgBVz\nzxquAdwdEXdHxOyIuCkirsrt23N7mYh4PSJOyPo+FhEdpHBWnCUcB1wxiPqOBH4WEedFxMyIOJk0\nQ3pUod/ywMXAFsBHIuLxrK5HgD8An8/1PQC4LiKeyh1jYkT8NiIeiogjgIeAQwdRp5mZmdUhP/M4\nhCStB2xJmqkjIt6QdAlp9vFW0q3sSyW1AtcBv4uIP/cz3tdIIa0ZeDuwHHB3bvs7gbGkwFrW+qSZ\nx7zbgf0LbWcB84CtI2JuYdsvgB9J+gawLCnQHpTVtArwLuBPVY7RMnB5p5Iya15b9jIzM1u6dXR0\n0NHR0att7tzi/0wPL4fHoXUQMAp4SlK+fb6kQyLimuzW7W7AfwI3SjozIo4uDiRpH2AScDhwBynI\nHU0Kpz12BR6IiCeG4VyuAdqBHYFLC9suA34MfAJYAXiNwc1+9uNIYN+hGcrMzGwJ09bWRltb7wmV\nzs5OWltbR6wG37YeIpJGAfsBRwCbFl5Pkk2dRcSzEXFhROxPWiH9pT6G3Bq4PSLOjoh7s9vFHyj0\nGQdcPshSHwS2KbRtA0wvtP2GNOt5oaQ98xsi4l/AZNKM5wHAlJ7FOBHxDPBslWNsXeUYZmZm1mA8\n8zh0dgdWBs6LiHn5DZIuA74g6X3AVOABoIk0c9dXoHoY2E/STqQvD9+P9PzhI9mYo0gzj5MGWeck\n4HxJ95GeXfwUaSa0GPaIiIslLQNMkbRPROS/t/JcoJP0nOYRhV1/AHxL0mzS91Z+Bfgg6RqZmZlZ\nA3N4HDoHAtcXg2PmUuB/SLd2TwTWJH2Vzm30fpgvv9r6bGAz4FdZewfpOcRds+0fBeZFxD2DKTIL\nhKsCx2bj/Q34bETcWa2OiOjIguqvJH06Iq7O2u+XdDewTETcVzjMJOAdpO+afDcpQO7Ws+jGzMzM\nGpfSd1Nbo5F0OjAqIg6p0fGXIc2Ifj8izh6C8cYAU9Pd8DLPPHYCrUydOpUxY/xzhmZmtvTKPfPY\nGhGdw308zzw2rvuAPldqDydJFdKzju8kpT0zMzNbSjg8NqiIOKcWx5W0POlnFP8JHBQRLw/tEWaR\nZhUH0jW0hzUzM7NSHB5tUCJiPsO6Sn989hpYU9NoKpXK8JViZmZmi3B4tLoyefJkWlpKfJc4UKlU\naG72Lx6amZmNJIdHqystLS1eAGNmZlbH/CXhZmZmZlaaw6OZmZmZlebwaGZmZmalOTyamZmZWWkO\nj2ZmZmZWmsOjmZmZmZXm8GhmZmZmpTk8mpmZmVlpDo9mZmZmVprDo5mZmZmV5vBoZmZmZqX5t62t\nrnR1dQ3r+JVKhebm5mE9hpmZ2ZLM4dHqSnt7+7CO39Q0mhkzuhwgzczMFpPDo9WZicBuwzR2F93d\n7cyZM8fh0czMbDE5PNqAJC0A9oyIK4b/aGsBY4b/MGZmZrZYltgFM5LOl7RA0hvZvz3vf1/r2gZD\n0vGSLsjez5J0WK1rMjMzs6XXkj7zeDVwAKBc2/zFHUzSqIh4460WNUjjgO+P8DHNzMzMqlpiZx4z\n8yPimYh4OveaK2nNbCZyk56OklbK2rbLPo/NPu8i6S5J3cA22baDJc2UNF9Sl6Reqzyy/b4i6feS\nXpH0N0mfKvT5d0kXS3pe0rOSfidpzWIfYAPgmjInm9X8F0ndkp6UdKKkZXLbb5Z0uqSTs2M+JWlC\nYYx1JN0q6VVJ90vascpxNpJ0Y3ZucySdLekdue3nS/qtpCOzOuZIOlPSqDLnYWZmZvVrSQ+P/YmS\n/U4EvgG0ANMk7QX8CJgEbAj8HDhf0tjCft8Bfg1sAkwBfiVpPQBJbwOuBeaSAunWwDzgmmxbjz2A\nWyLi5YGKlPRe4CrgL9kxvwIcBHyr0HV/4CVgS+Bo4HhJO2RjCPgt0A1skY1xMrlrJWl0VvuzQCvw\naWBH4MeF42wPrA18NDvmAdnLzMzMGtiSHh53lzQv93pR0jHZNvW750LjI+LGiJgVES8ARwLnRcTZ\nETEzIk4DLgOOKux3SUScn/U5HrgLODTbtg+giPhSREyPiBmkoNdMCls9xgFlF6l8DZgdEYdFxEPZ\n4pYJWb150yJiYkT8LSIuzOraIdv2n8AHgf0i4v6I+CNwLL2v1b7A8sD+EdEVEbcAhwD7S1ol1+85\n4JCslt+Tgu0OmJmZWUNb0p95vIk0e5YPP88BK5XcP4CphbYW4OxC2+1AcSHLHYXPfwY2zd5vAqwr\naV6hz/LAB4AbJL0TGAscWLLW9bNjFOtaQdK/R8Tfs7ZphT5PAavmxng8Iv5ZqLt4nHsjortwnGWA\n9YBnsrYHIiI/u/sUsNHAp3EqcHGhrS17mZmZLd06Ojro6Ojo1TZ37twRrWFJD48vR8SsYmMWzKB3\nqFy2rzGGvCpYgTTj91kWnQHtCV+7kgLYE0N87NcKn4PhmYFezOMcSZrcNDMzs6K2tjba2npPqHR2\ndtLa2jpiNSzpt6370hPQ3pNr25xyz0F2kS2cydkGmF5o26rK557f3usE1gWeiYhHCq+e2chxwOUl\n6snX9eFC20eAeblZxzJjrCFptVxbccwuYFNJby8c5w1gxiDqNTMzswa0pIfH5SWtVni9O7vlegdw\njKT1s8UuE6vsX+25yEnAAdlq6nUkHQHslbXnfUbS5yWtK+kE0gKUM7NtU4A5wOWSPiLp/ZI+mq2E\nfm+2KnlXqj/v+D5JmxZeKwM/IQW/H0taT9I44Nuk+8Bl3QA8DFwgaRNJ2wLfpXeonkJaUPNLSRtK\n2h44A7ggIp5ZZEQzMzNboizp4XEX4MnC67Zs24Gk2/Z3AT8Ejquy/yIzkRFxOfDfpPur9wNfBA6I\niNsKXSeQFsbcC7QD+0TEg9kYrwLbAbOBS0mzlr8gPfP4ImnRzLyIuKdKTUeRZi7zr90i4knS7/pt\nAdxDCpO/AL7X3/kUzi2APYEm0qrtn5MWzOT7vArsDLwLuBO4BLiehYuBzMzMbAmm3msabCi81Z/z\nk3Q6MCoiDhnayuqXpDHAVJjM8D3z2Am0MnXqVMaM8U8gmpnZkiH3zGNrRHQO9/GW9AUzjeo+Fl3l\nbGZmZlZzDo/D4y1N50bEOUNVSOOZRZohHA5dA3cxMzOzfjk8DoOI8M/wLbbx2Wt4NDWNplKpDNv4\nZmZmSzqHR6srkydPpqWlZdjGr1QqNDc3D9v4ZmZmSzqHR6srLS0tXsxiZmZWx5b0r+oxMzMzsyHk\n8GhmZmZmpTk8mpmZmVlpDo9mZmZmVprDo5mZmZmV5vBoZmZmZqU5PJqZmZlZaQ6PZmZmZlaaw6OZ\nmZmZlebwaGZmZmalOTyamZmZWWn+bWurK11dXbUuoU+VSoXm5uZal2FmZlZTDo9WV9rb22tdQp+a\nmkYzY0aXA6SZmS3VHB6tzkwEdqt1EVV00d3dzpw5cxwezcxsqebwuISSdDNwd0Qc8RbHOR9YKSI+\nOTSVDWQtYMzIHMrMzMwGzQtmGoik/5W0QNJPqmw7K9t2Xta0FzB+CA57GHDAEIxjZmZmSwCHx8YS\nwGxgH0nL9zRm79uAx97sGPFCRLz8lg8YMS8iXnyr45iZmdmSweGx8dwNPA7kbyN/khQc7+5pkHSz\npB/mPn9V0kOSXpX0D0mX5LZ9WtI0Sa9ImiPpOklvz7adL+mywrinSzpZ0rOSnpI0IV+gpPUk/TE7\n1n2SPprNiu4x5FfDzMzMRpTDY+MJ4DzgwFzbgcD5gKrtIOlDwOnAt4APAjsDt2bbVgcuAs4B1gfG\nApf1NVZmf+AlYEvgaOB4STtk4y0DXA7MA7YAvgyclNVtZmZmDc4LZhrTFOAkSWuQ/g/A1sDewPZ9\n9F+DFPauym5lPw7cm217DzAK+G1EPJ61PTDA8adFxMTs/d8kHQLsANwI7ERa9bJtRDwDIOk44PrB\nnaKZmZnVI4fHBhQRcyT9H/B50gzhVRHxnNTnZOH1pNvasyRdA1xDCouvkkLkjcD9kq4FrgN+ExEv\n9FPCtMLnp4BVs/cfBB7vCY6ZO8uf3anAxYW2tuxlZma2dOvo6KCjo6NX29y5c0e0BofHxnU+cCbp\ndvBX++sYES9JGgN8lDQzeALwbUkfyhbD7CTpw9m2Q4HvSdoyIh7rY8jXiodgyB6BOBLYd2iGMjMz\nW8K0tbXR1tZ7QqWzs5PW1tYRq8HPPDaua4DlSP8H4LqBOkfEgoi4KSKOATYF3g98LLf9zxFxArA5\n8C/SV/0sjhnAGpJWybVtuZhjmZmZWZ3xzGODiogFktbP3ve7GEXSx4G1SYtkngc+TrrdPUPSlqTn\nFa8Dnga2AirA9MUs7XrgEeACSUcDKwLfJc1OetGMmZlZg3N4bGAR8VK15iptL5C+zmcC0AQ8DOwT\nEV1ZAN0O+G9S0HsMOCIi+prN7DcAZqF2HGn19p2kIHk0cCXQPeBJmZmZWV1zeGwgEfH5AbbvlXu/\nfe797fSxEjsiHgR2LXvMiPhYlT57FT4/RAqkAEjahhQ6Z/ZXv5mZmdU/h0cbcpL2JH010MPAusCP\ngD9GxKyaFmZmZmZvmcOjDYd3AieTvl9yDuk5yKPK7ToL6Byuut6CrloXYGZmVhccHm3IRcSFwIWL\nt/f47FV/mppGU6lUal2GmZlZTTk8Wl2ZPHkyLS0ttS6jqkqlQnNzc63LMDMzqymHR6srLS0tjBkz\nptZlmJmZWR/8JeFmZmZmVprDo5mZmZmV5vBoZmZmZqU5PJqZmZlZaQ6PZmZmZlaaw6OZmZmZlebw\naGZmZmalOTyamZmZWWkOj2ZmZmZWmsOjmZmZmZXm8GhmZmZmpfm3ra2udHV11bqEmqhUKjQ3N9e6\nDDMzswE5PFpdaW9vr3UJNdHUNJoZM7ocIM3MrO45PFqdmQjsVusiRlgX3d3tzJkzx+HRzMzqnsPj\nEJE0ARgXEWNqXUtjWwvwJTQzM6tXDb9gRtJWkl6XdGWNS5kE7DDUg0oaK2l29v5/JS2Q9Iakf0n6\nh6TrJH1ekob62GZmZmZFDR8egYOAM4DtJK1eiwIkjYqIVyLi+WEYfg/giux9AFcDqwNrArsANwGn\nA1dKqvu/pyTPdpuZmTWwug8b/ZH0DmBv4KfAVcABuW1js1m6nSR1SnpF0g2SVpG0q6TpkuZKmiKp\nKbefJH1T0iPZPndL+lSVcXeRdJekbmAbSRMk3V2o70BJ90vqlvSEpDNy2w6XNE3SS5JmSzpL0ugq\np7kHcHnu8/yIeCYinoqIeyLiJGAc6UHB/PmvJOkcSU9n53mDpE1y2ydk59YuaZakFyR1ZNcUSV+U\n9ESVa365pHNyn8dJmirpVUkzJR0vaVRu+wJJX8n2mwccW+UczczMrEE0dHgkBceuiHgYmEKahSya\nAHwV+DDQDFwCHAbsQwpcOwGH5vofC7QDXwI2AE4DLpS0bWHcE4FvAC3AtKwtejZKOhg4E/gZsCHw\nceCh3P5vZMfdANgf2B44JX8ASRsCqwA393cRIuJm4F7gk7nm3wDvBnYmPUTYCdwgaeVcnw+wMHh+\nHBgLHJNt+zXwLknb5+r5t2y8ydnnbYFfkq7R+sCXgc+xaECcAFwGbAyc19+5mJmZWX1r9FuIBwIX\nZu+vAVaUtF1E3Jq1BXBcRNwBIOlc4PvA2hHxWNb2G1JwmyRpOeCbwA4R8ZdsjEezkPRl4LbcscdH\nxI09H6pj36pzAAAgAElEQVQ8cngcMCkizsy13dPzJiLOyLXPljSeNIN6SK59D+DaiHh94EvBg6Rw\nhqSPAB8CVo2I17LtR0vaC/g00DNzKOBzEfFKtt+FpOc2x0fEC5KuAT7LwvD6GeCZiLgl+3w8cGJE\nTM4+PybpeFIInpirbUpE/LLEOZiZmVmda9jwKGk9YEtgT4CIeEPSJaTZx1tzXe/Lvf8n8EpPcMy1\nbZG9XwcYDVxfWICyLGnmrkcAU/upbRXgvaTnEfvqsyNplm99YEXS32J5SU0R0Z11Gwf8uK8xikOy\ncOZzE+CdwHOFUNtEmm3s8WhPcMw8Baya+zwF+Lmkr2Yh9LPAr3LbNwW2lvStXNsoYLnCefR5rRZ1\nKnBxoa0te5mZmS3dOjo66Ojo6NU2d+7cEa2hYcMjKSSOAp4qBKT5kvKzd6/l3kfhc09bz+37FbJ/\ndwOeLPSbX/j8cj+1vdrPNiStCVwJnEW6xfscsC1pRnA5oDtb/LMZ6VnOMlqAWdn7FUj1jyWFyrwX\ncu/7uxZkNS4DfFzSXVmNX89tX4E0+3hZsZhccIT+r1XBkcC+5bubmZktRdra2mhr6z2h0tnZSWtr\n64jV0JDhMVuQsR9wBHB9YfPvSNNUMxZj6OmkkLhmRPxxceuLiJckPUq6BfyHKl1aAUXEUT0NkvYp\n9Nkd+FNEvMAAJH2MdMv61Kypk7Qi+42ImD34M0giYr6ky0jPgK4LPBgR9+S6dALrRcQji3sMMzMz\naywNGR5JwWpl4LyImJffkIWdLwD/w6Kzbv3KQt8PgNOygPpHYCVgG2BuRPQ8X1lm3G8DP5X0DOnr\ndVYEts6egZwJLCvpMNLs3kdIz1Tm5b+iJ295SauRZl1XA3Yl3f6+guz5z4i4QdKfgd9J+gZpoc77\nSDOql0VEZ5Vx+zIF+D/Sop8LC9u+Q/qKoMdJC3QWkG5lbxQR4wdxDDMzM2sQjbra+kDg+mJwzFxK\nmtnbmNzq57Ky0DORFMimk4Lfbiy8JUyZcSPiAtIt3oOB+0nhbp1s2zTSrOnRpGcy21i4yrnnK4h2\noHp43IV0S3pWVttY4JCI2DMi8nXtRnr28zzSLOxFpNXm/xyo9oKbSLfV183GyJ/jdcAngP8E7gT+\nnJ3zo/lugzyemZmZ1TH1zhtWD7JV0RMjYqNa1zJSJI0BpqZvAVrannnsBFqZOnUqY8b4pxnNzGxw\ncs88tg7y7uJiadSZxyXdPNJ3SJqZmZnVlUZ95nGJFhE31LqG2plF729FWhp01boAMzOz0hwerc6M\nz15Ll6am0VQqlVqXYWZmNiCHR6srkydPpqWlpdZljLhKpUJzc3OtyzAzMxuQw6PVlZaWFi8aMTMz\nq2NeMGNmZmZmpTk8mpmZmVlpDo9mZmZmVprDo5mZmZmV5vBoZmZmZqU5PJqZmZlZaQ6PZmZmZlaa\nw6OZmZmZlebwaGZmZmalOTyamZmZWWkOj2ZmZmZWmn/b2upKV1dXrUuwGqhUKjQ3N9e6DDMzK8Hh\n0epKe3t7rUuwGmhqGs2MGV0OkGZmDcDh0erMRGC3WhdhI6qL7u525syZ4/BoZtYAGjo8SpoAjIuI\nMbWuZUkn6Wbg7og4YniPtBbgP6eZmVm9WqwFM5K2kvS6pCuHuqBBmgTsMNSDShoraXb2/n8lXTbU\nxxhkPQ9KelXSqrWsw8zMzGxxV1sfBJwBbCdp9SGspzRJoyLilYh4fhiG3wO4InsfwzB+aZK2AZYH\nfgMcUMtazMzMzAYdHiW9A9gb+ClwFblAk83YLZC0k6ROSa9IukHSKpJ2lTRd0lxJUyQ15faTpG9K\neiTb525Jn6oy7i6S7pLUDWwjaYKkuwv1HSjpfkndkp6QdEZu2+GSpkl6SdJsSWdJGl3lNPcALi95\nPdaQdLmkedm5XZyfIZS0iaSbJL2Ybf+rpMHclz0IuAiYDBxY5fizsmt3bnaMxyR9sdBn6+yavirp\nDkm7Z9dzk1yfjST9PjuPf0i6QNK7+znv5ST9QNLfs+v5Z0ljc9ubJV0h6bls+32SdhnEeZuZmVkd\nWpyZx72Broh4GJhCCjdFE4CvAh8GmoFLgMOAfUirIXYCDs31PxZoB74EbACcBlwoadvCuCcC3wBa\ngGlZ25szg5IOBs4EfgZsCHwceCi3/xvZcTcA9ge2B07JH0DShsAqwM39XoXUV6QZypWBbYEdgbWB\ni3PdpgCPA62kh/lOAl4baOxs/BWAzwAXAtcDK2UzkUVHAH8FNgN+AvxU0rrZGO/MarwX2Jz0tzmF\n3tdtJeBGYGpW487AqqS/W1/OAv4D+C9gY+DXwNWSPpBt/wmwHPARYCPS3+2lMudtZmZm9WtxFswc\nSAozANcAK0raLiJuzdoCOC4i7gCQdC7wfWDtiHgsa/sNKbhNkrQc8E1gh4j4SzbGo1lw/DJwW+7Y\n4yPixp4PKbv1chwwKSLOzLXd0/MmIs7Itc+WNJ40g3pIrn0P4NqIeH3gS8GOpJD6/oh4Mqtpf+AB\nSa0RMZUUnk/JwjbA30qM26MNeCgiHszG7iCF9dsL/a6KiJ9l70+WdDjp+j4M7AssAL4UEf8CHpT0\nA+Dnuf0PATojYnxPg6QvkK7ROhExM38wSc2kGec1IuIfWfMPJe0KfB74FrAG8JuImJ5tf3QQ521m\nZmZ1alDhUdJ6wJbAngAR8YakS0iB5tZc1/ty7/8JvNITHHNtW2Tv1wFGA9erdxpcFujMfQ7SzFhf\nta0CvBe4qZ8+OwLHAOsDK5LOf3lJTRHRnXUbB/y4rzEK1gce7wmOABHRJekF0uzoVOCHwLlZqLwB\n+HVEPFJy/M+Tblf3uAi4RdKhEfFyrv2+3rvxD9LMIcAHgWlZcOxxJ5C/1psCH5M0rzBOAB8AZhba\nNwJGAQ8V/mbLAXOy92eQZkB3Jp33pRFRrLOKU+k9cQspQ7cNvKuZmdkSrqOjg46Ojl5tc+fOHdEa\nBjvzeBApNDxVmPWbLyk/e5e/LRsseps2WHjLfIXs392AJwv95hc+v0zfXu1nG5LWBK4k3W49FniO\ndKv5HFLo6VZa/LMZ6VnOIRERJ0iaQrqFvhvwbUn7RES/z1RKagG2AraQlL+1vgzp9v+5ubb+rm8Z\nK5BubR9N71AJ8FQf/V8n3eJeUNj2EkBEnCvpGtJ57wQcI+nIiDir/1KOJE2WmpmZWVFbWxttbb0n\nVDo7O2ltbR2xGkoHDEmjgP1Iz9dtWng9yeJPDU0nhcQ1I+KRwuuJsoNExEukW6N9fXVPK6CIOCoi\n7sxuxb6v0Gd34E8R8ULJw3YBa0h6cxxJG5Cegey5XUtEzIyI0yNiZ+C3pBnFgRwE/AHYhN7X+jSq\nP2falxnAxpKWzbVtSe9V5J2k2++PVfkbVAvld5P+T8RqVfo/3dMpIp6IiJ9HxKdJM7BfrDKWmZmZ\nNZDBzDzuTgpF50VEr9ubSt+D+AXgf1h05qpfEfFS9gzeaVlA/SOwErANMDciep6vLDPut0m3Sp8B\nribdmt46ewZyJrCspMNIM5AfIT1TmZf/ip68lSVtWmh7NiJukHQ/MCV7znBZ0szmzRHRqbSifBLp\na3ZmkZ4D3IK0uKRPkt5GCurfioiuwrZzgCMktRS39eEi4HvALySdBKxJmt6DhQHyLNLf71fZLOdz\nwLqkxVEHRUSvryuKiIclXQRcIOkoUphcFfgYcG9EXC3pNNLf4CHgXaRnMKdjZmZmDW0wtzYPBK4v\nBsfMpaSZvY1ZjO9FzBZqTCQ9jzidFDp2IwWuN7uVGOcC4OvAwcD9pCC4TrZtGmnW9GjSM4Jt2fGA\nN7+CaAeqh8expNm5/Ov4bNsewPOkWcLrSCF1n2zbG8C7gV+SZgB/Rbol/u0BTmUPUuD6XZVzfJB0\njXpmH6tdl8j1nwd8gjRreTfpOp+Qbe7O+jxFCuvLANeSVrL/EHg+FxyLxzkAuAD4AfAgcBnwIWB2\ntn0UaeX7dOD3WZ+vDXDeZmZmVudUmFRaaknaC5gYERvVupbhJmlf0jOTK0VE8bnSmsi++3JqWh/k\nZx6XLp1AK1OnTmXMGP80pZnZYOWeeWyNiM6B+r9VDf3b1kNsHum7CJc4kvYDHgGeIC0IOgm4uF6C\no5mZmTUOh8dMRNwwkseT9HvSau9FSgG+HxEnDeHhVge+A6xGWj19Mem7GOvQLHp/Q5Mt+co8umtm\nZvXC4bF2DgLe3se254byQBExibRwpwGMz162NGlqGk2lUql1GWZmVoLDY41ki1SsYPLkybS0tNS6\nDBthlUqF5ubmWpdhZmYlODxaXWlpafGiCTMzszo2mK/qMTMzM7OlnMOjmZmZmZXm8GhmZmZmpTk8\nmpmZmVlpDo9mZmZmVprDo5mZmZmV5vBoZmZmZqU5PJqZmZlZaQ6PZmZmZlaaw6OZmZmZlebwaGZm\nZmal+betra50dXXVugSrI5VKhebm5lqXYWZmOQ6PVlfa29trXYLVkaam0cyY0eUAaWZWRxwerc5M\nBHardRFWF7ro7m5nzpw5Do9mZnXE4XExSJoAjIuIMXVQywJgz4i4oo/tY4GbgH+LiBdHtLjFshZQ\n88tqZmZmfWioBTOStpL0uqQra1zKJGCHoR5U0lhJs3OfV5P0Y0l/k9Qt6TFJV0j62CCGvR14T2ME\nRzMzM6t3jTbzeBBwBnCQpNUj4h8jXYCkURHxCvDKMAy/B3BFdpz3k4Lfc8CRwP3AssAuwJnABmUG\njIjXgaeHvlQzMzNbGjXMzKOkdwB7Az8FrgIOyG0bK2mBpJ0kdUp6RdINklaRtKuk6ZLmSpoiqSm3\nnyR9U9Ij2T53S/pUlXF3kXSXpG5gG0kTJN1dqO9ASfdnM4RPSDojt+1wSdMkvSRptqSzJI2ucpp7\nAJdn738CvAFsERG/i4iZEdEVEacBWxX2W0XSZZJelvSQpN2rnMOKubZtJN2c9X9O0tWSVsq27Szp\nNknPS5oj6UpJaxfOdevsWr0q6Q5Ju2fH2KRw3L9k1+NJSSdKapj/3szMzKy6Rvof872Broh4GJhC\nmoUsmgB8Ffgw0AxcAhwG7ENahbETcGiu/7FAO/Al0kzeacCFkrYtjHsi8A2gBZiWtUXPRkkHk2YD\nfwZsCHwceCi3/xvZcTcA9ge2B07JH0DShsAqwM2S/g3YGTgzIrqLJ1nlFvTxwK+AjYHfA1MkrZzf\nJXeczYAbSDOZW5Gu1eXAqKzLO4BTSQ8efiyr/be5/d9Jmh29F9icdM1PKRzjvaSA/xdgE+ArpL/X\nt4rnYmZmZo2lkW5bHwhcmL2/BlhR0nYRcWvWFsBxEXEHgKRzge8Da0fEY1nbb0jBbZKk5YBvAjtE\nxF+yMR7NguOXgdtyxx4fETf2fJBUrO04YFJEnJlru6fnTUSckWufLWk8aQb1kFz7HsC1EfG6pHUA\nATMGuiiZ8yPikqy2Y0mBeUvguip9/wf4a0TkQ/Sbx4mIy/KdJX0BeFrSBhExHdgXWAB8KSL+BTwo\n6QfAz3O7fQ2YHRGHZZ8fyhYZnQR8p+Q5mZmZWR1qiPAoaT1SGNoTICLekHQJaTbr1lzX+3Lv/wm8\n0hMcc21bZO/XAUYD16t3GlwW6Mx9DmBqP7WtAryXtKK5rz47AscA6wMrkq778pKacjOL44Af9+zS\n11h9ePO8I+IVSS8Cq/bRdzPSjGxfta5DCnj/AVRIs9NBmsmdDnwQmJYFxx53FmpeH/hzYejbgRUk\n/XtE/L3vUzkVuLjQ1pa9zMzMlm4dHR10dHT0aps7d+6I1tAQ4ZEUEkcBTxVm/eZLys/evZZ7H4XP\nPW09t+pXyP7dDXiy0G9+4fPL/dT2aj/bkLQmcCVwFuk2+XPAtsA5wHJAt6TVSaHuqmy3h7Na12fh\nM5D96e88B1Uv8H/ALOALpOuyDPBAVusIOJI0uWlmZmZFbW1ttLX1nlDp7OyktbV1xGqo+2ceJY0C\n9gOOADYtvJ5k8aekppNC4poR8Ujh9UTZQSLiJeBR+v7qnlZAEXFURNwZETOB9xX67A78KSJeyMZ8\nHrgW+JqktxcH7Fncspim9VWrpHeRZha/GxE3R8QM4N2FbjOAjSUtm2vbktwzj0AX6VnKvI8A8/qf\ndTQzM7N6V/fhkRSsVgbOi4jp+RdwGWmGDAZ5qzcLfT8ATpO0v6S1JW0u6RBJ++W6lhn328CRkg6V\ntI6kMbkZ0ZnAspIOk7RWNvaXC/u/+RU9OV8jzbbeKemT2bjrSzoM+NNgzrVwDicCW2QrvjfOxvxK\nFhyfB54FviTpA0rfJ3kqvYPhRVldv8j23Zk0XUiu30+ANZS+o3I9SeOya3TqIOs2MzOzOtMI4fFA\n4PqImFdl26Wkmb2N6R1wSomI8aTfwzuGNBN5Nek29qx8txLjXAB8HTiYtIr5CtIzlUTENNKs6dGk\nZxPbsuMBb34F0Q4UwmNEzCKteL6ZFHLvIy2A2Skbr7/6im1vfs5Wq+9EWgX9F9KziHsAr0dEkFa1\nt2bHOxU4qlDXPOATpJnfu0nX74Rsc3fW50nSddyCtHDoJ8AvgO9VqdXMzMwaiFJesFqRtBcwMSI2\nqnUti0vSvsC5wEoRUXxetOwYY4CpMBk/82hJJ9DK1KlTGTPGP1lpZtaX3DOPrRHROVD/t6pRFsws\nyeaRvkOyYWS33h8BniAt9DkJuHhxg6OZmZk1DofHGouIG2pdw2JYnfR1PqsBT5G+W2eIvgB8Fr2/\nKcmWXl21LsDMzKpweLRBi4hJwKThGX189jKDpqbRVCqVWpdhZmY5Do9WVyZPnkxLS0uty7A6UalU\naG5urnUZZmaW4/BodaWlpcWLI8zMzOpYI3xVj5mZmZnVCYdHMzMzMyvN4dHMzMzMSnN4NDMzM7PS\nHB7NzMzMrDSHRzMzMzMrzeHRzMzMzEpzeDQzMzOz0hwezczMzKw0h0czMzMzK83h0czMzMxK829b\nW13p6uqqdQlmpVUqFZqbm2tdhpnZiHJ4tLrS3t5e6xLMSmtqGs2MGV0OkGa2VHF4tDozEdit1kWY\nldBFd3c7c+bMcXg0s6WKw2ODkzQLOC0izqh1LUNjLWBMrYswMzOzPnjBTI1IulnSD6u0f07S87Wo\nyczMzGwgDo/1KWpdgJmZmVk1Do91TNL5kn4r6UhJT0qaI+lMSaP62ecLkp6XtH32+WZJp0s6WdKz\nkp6SNKGwzxqSLpc0T9JcSRdLWjXbtqKk1yWNyT5L0nOS/pTbv13S7Oz9mpIWSNpL0k2SXpZ0j6St\nhuMamZmZ2chyeKx/2wNrAx8F9gcOyF6LkHQ08H1gx4i4Obdpf+AlYEvgaOB4STtk+wi4AlgZ2BbY\nMTvexQAR8SJwd3Z8gI2BBcDmkkZnbdsBtxTK+S5wCrAp8BBwkST/92ZmZtbg/D/m9e854JCIeCgi\nfg9cBexQ7CTpZOAwYLuImFrYPC0iJkbE3yLiQuCu3Bg7AhsCbRFxT0T8lRQ2x0pqzfr8gYXh8aPA\ndUAX8JFc2x8Kx5wUEddExExgArAmsM5gT97MzMzqi1db178HIiL/DORTwEaFPkcBo4EPRcSjVcaY\nVvj8FLBq9n594PGIeLJnY0R0SXoBaAGmkoLhgdks5VjgWuAfwEcl3UcKhbcUjnFf4XjKjvlQn2cK\nwKlkk545bdnLzMxs6dbR0UFHR0evtrlz545oDQ6PtfMisFKV9pWB/H8FrxW2B4vOGN8KfBzYGzi5\nyphlxujPrcA7gVbSLepvAv8EjiEF0yci4m/9HLMn/JY45pHAvoMozczMbOnR1tZGW1vvCZXOzk5a\nW1v72GPo+bZ17cyg+hcatjLg7Nwi7gR2BY6VdOQg9+0C1pD0vp4GSRuQQux0gIiYS5pJPAT4V0Q8\nRAqUmwOfYNFb1l4tbmZmtoRyeKydnwIflPQjSRtL+qCkI0izhz8Y7GARcQfpp1mOl/Tfg9jvBuB+\nYIqkzSVtCfwSuDkiOnNdbyFNCf4h2+95UvDcm0XDowZbv5mZmTUGh8caiYhZpFvA6wPXA3cAnwY+\nHRHXD2ao3Ji3k2YCJ0r62iDG2AN4nhQCrwNmAvsU+vyB9N9LfhX3LVnbLX3VNECbmZmZNRg/81hD\n2aroXfrZ/vkqbYcXPq9d+HwbsGLu8/ZVxtir8PnvwF7FfoU+lwOjCm2HA8V6HqvSb26xzczMzBqT\nZx7NzMzMrDTPPFqdmQV0DtjLrPa6al2AmVlNODxanRmfvczqX1PTaCqVSq3LMDMbUQ6PVlcmT55M\nS0tLrcswK6VSqdDc3FzrMszMRpTDo9WVlpYWxoyp9vWXZmZmVg+8YMbMzMzMSnN4NDMzM7PSHB7N\nzMzMrDSHRzMzMzMrzeHRzMzMzEpzeDQzMzOz0hwezczMzKw0h0czMzMzK83h0czMzMxKc3g0MzMz\ns9IcHs3MzMysNP+2tdWVrq6uWpdgZmZLmUqlQnNzc63LaBgOj1ZX2tvba12CmZktZZqaRjNjRpcD\nZEkOj1ZnJgK71boIMzNbanTR3d3OnDlzHB5LcngEJE0AxkXEmFrXsqSQ9DhwYkT8ZHB7rgX4z2Bm\nZlavarpgRtJWkl6XdGUt6wAmATsM9aCSxkqanb3/X0kLJB1d6DNO0oKhPnaJ2r4l6UlJKxfaN5XU\nLcnTf2ZmZvb/7d19kFxVmcfx788QEgOFqEEiq+ALAkEwmuFlFQiRpABBgkitEN1lLXR5B3XdTQIr\nIlIoUry/SmGJSxKioK5AibAEBHnVzUwkINmsFuFFXgJIHEkCAZJn/zinyc1NZ9IDPX1nun+fqlvp\ne+7p2+c86el5+txz7qyj6tXWXwIuAiZIGlNFAyQNi4gVEbF0AE4/BbghPw7gJWC6pLeV6sUAvPaG\nfBd4HHh9ZFDSRsCPgKsj4qY3clJJw5vSOjMzMxuUKkseJW0CHAZcDvwS+GLh2N55lG5fST2SVkia\nK2kLSZ+S9LCkXkmzJY0sPE+STpb0SH7OfEmH1jnv/pLmSXoZ2EPSaZLml9p3pKSH8ijck5IuKhz7\nmqQFkpZJelzSpZJG1enmFOD6wv5c4BnglA3EZk9Jv8l9eEzShZLemo8dL+nBQt3P5D4dVSi7VdK3\n+3qNiFgF/DNwsKTP5uJvAG8D/rVwro9Iuj235TlJl9fako/PlHSdpFMlPQU8tJ4+HSNpqaQJfbXL\nzMzMBrcqRx4PAxZGxB+B2aRRyLLTgOOAjwNbA9cCJwGHk1ZV7AucWKh/CvCPwFHAjsD5wExJe5XO\n+11gOjAWWJDLXh/9k3QscAnwfeDDwIHA/xWevyq/7o7AEcAngbOLLyDpw8AWwK9LzzsFOFHSVnX6\ni6QPAr8CrgN2IsVpj9wegDuBsZLemfcnAM8BE/PzNyLFq/i6dUXEotyeyyXtC8wAvhgRy/K5NgFu\nAZYAXbkt+wEXlk61H/A+YB/gM3X6dApwOrBPRPxmQ+0yMzOzQSwiKtmAu4ET8uNhpARlQt7fm5Ro\nTSzUn57LtimUXQ7clB9vDCwDdi+9zpXArMJ5VwOfLtU5Degp7P8ZOL0ffTkUeLZUdjLwk8L+VcDP\n8+N7gSvz44OBVaX2Xl46157Aa8DGef854LP5cQ8wDXgy7+8BvAyM7Ef7b8/nP7dUfizwbO11c9lB\nwCvAO/L+TOAJYFjpuU8AxwPnkC6Pb7eBNowHAmYFhDdv3rx589airTuA6O7ujqGquzv1ARgfMfA5\nXCWrrSVtD+xGHqWKiFWSriWNPhZHph4sPF4CrIiIx0plu+bH2wKjgFslqVBnOCnBqgmgu4+2bQFs\nRUqo1ldnMmmUbgdgM9Kq9RGSRkbEy7nawcDF6znFdOA2SefUOTYO2FlS8YaHtf68H1hEitFESbeR\nRk8vA6ZJ2o40Evk/hXY04kxSYn1mqXwHYH5EvFIou4fU3+2A+3PZgkiXwcumAyOBXSLi8caaci7w\nk1LZ1LyZmZl1tjlz5jBnzpy1ynp7e1vahqpu1fMl0mjj02vneayUdEJh/9XC4yjt18pql943zf8e\nADxVqreytL+8j7a91McxJG0D3AhcSrrk+wKwF/AD0ujny3nxz0dJcznXERF3SboFOIu0QKVoU+AK\n0qVhlY7VErA7gH/Jrzs/IpZJuot0+Xxv0qXt/nit9G9/rS+ed5JGKv+BlBU24OvAF95gM8zMzNrb\n1KlTmTp17QGVnp4eurq6WtaGliePkoYB/0RalHFr6fAvSENMi97AqR8mJYnbRMTdb7R9ORF7lHTr\nnnpJWBegiPi3WoGkw0t1DgLujYi/9vFSJwO/Z92+9gA7RsTiPp57J3ABKSm7I5fdAUwGPkG6VNwM\nC4GpkkZERC0Br11Cb+T/6D7S1IKbJb0WEeW5kmZmZjbEVDHyeBCwOfDDiHixeEDSz4EvA//OuqNu\nfcpJ3znA+TlBvZu0cngPoDciZtZepoHTfYu0iOQ50uKVzYBPRMQlwJ+A4ZJOIo1A7gkcXXp+8RY9\n62vvQ5JmkxYAFX0PuE/SxaTRzOWkRTuTI+LE/NwFkpaSEu1P5+fdQUoaV5MuLfdXvbjMBL4J/EjS\nGcC7SUnrVdHgrY0i4l5JBwK/lLQqx9DMzMyGqCpWWx8J3FpOHLOfkUb2dob+3/swIk4l/X27GaSR\nyF+RLmMXR/E2eN6IuBr4KmnByEOkRHDbfGwBadR0GmlO5tT8esDrK5QnsYHkMfsm6f/g9TZFxIOk\nS88fIs1t7CEls0+WnnsXKVGsjbIuAHpJ8x37vPS+HuvEJSKWk1ZSbwnMA35MiulX+nO+iLiLlFB/\nR9Ixb6BtZmZmNkgoot85mvVB0iHAGRGxU9VtGUokjQe6YRae82hmZq3TA3TR3d3N+PFD88/jFuY8\ndkVEz4bqv1lV/4WZdvQiaZWxmZmZWduparV124qIuVW3oUbSQ8A2dQ4FcHREzKlzrGKLWfvOSmZm\nZgNpYdUNGHKcPLa3T5Huc1nPklY2pHGn5s3MzKw1Ro4cxejRo6tuxpDh5LGNRcQTVbehv2bNmsXY\nsQdSArwAAAdJSURBVGOrbkalbr75Zvbff/+qmzEoOBaJ47CGY5E4Dms0IxajR49m6623blKL2p8X\nzNigUFswM5QnLDfLlClTuOGGRhbrtz/HInEc1nAsEsdhDcfCC2bMzMzMbBBz8mhmZmZmDXPyaGZm\nZmYN84IZGyxGAixc6Fsm9Pb20tPj2xWBY1HjOKzhWCSOwxqOxVq/O0e24vW8YMYGBUmfB2ZX3Q4z\nM7Mh7AsRcc1Av4iTRxsUJL2T9He0HwVerrY1ZmZmQ8pI4H3ALRHxl4F+MSePZmZmZtYwL5gxMzMz\ns4Y5eTQzMzOzhjl5NDMzM7OGOXk0MzMzs4Y5eTQzMzOzhjl5tMpJOl7SYkkvSbpf0q5Vt6mZJJ0s\n6XeS/iZpiaT/krRdnXrflvSUpBWSbpW0ben4CEmXSnpe0ouSfirpXa3rSXNJmiFptaTzSuUdEQdJ\nW0mamfuxQtIDksaX6rR1LCS9RdIZkh7JffyTpG/Uqdd2cZC0l6QbJD2Zfw6m1Knzpvst6e2SZkvq\nlbRU0g8kbTLQ/WtUX3GQtJGk70laIGlZrvOfkt5dOseQjwM09p4o1P1+rnNSqbwlsXDyaJWSdBhw\nLnAa8DHgAeAWSaMrbVhz7QVcDOwOTAaGA/8t6a21CpKmAycARwG7ActJcdi4cJ4LgAOBQ4EJwFbA\nz1rRgWZT+oJwFOn/u1jeEXGQtDlwD7CSdH/TscDXgaWFOp0QixnA0cBxwA7ANGCapBNqFdo4DpsA\nvyf1fZ175jWx39eQ3l+Tct0JwBXN7Mib1FccRgEfBU4n/X44BNgeuL5Urx3iABt4T9RIOoT0++TJ\nOodbE4uI8Oatsg24H7iwsC/gz8C0qts2gH0eDawG9iyUPQV8rbC/GfAS8LnC/krgkEKd7fN5dqu6\nT/3s/6bAImAf4NfAeZ0WB+As4M4N1Gn7WAA3AleWyn4KXN1hcVgNTGn2/z8pQVgNfKxQZz/gNWBM\n1f1uJA516uwCrALe065x6CsWwN8Bj+c+LQZOKr1HWhILjzxaZSQNB7qA22plkd7Jc4GPV9WuFtic\n9K3yBQBJ7wfGsHYc/gb8ljVx2IX0t+iLdRaRPkSGWqwuBW6MiNuLhR0Wh4OAeZKuVZrK0CPpy7WD\nHRSLe4FJkj4EIGkcsAdwU97vlDispYn9/ntgaUTML5x+LunzZ/eBav8Aq31+/jXvd9EhcZAk4Grg\n7IhYWKdKy2KxUf+abtZUo4FhwJJS+RLSt6W2k3/4LwDujoiHc/EY0g9uvTiMyY+3BF7Jv0DWV2fQ\nk3Q46TLULnUOd0wcgA8Ax5KmbJxJuix5kaSVETGTzonFWaTRkv+VtIo0leo/IuLH+XinxKGsWf0e\nAzxbPBgRqyS9wBCMjaQRpPfMNRGxLBePoXPiMIPU10vWc7xlsXDyaNZalwE7kkZXOoqk95AS58kR\n8WrV7anYW4DfRcSpef8BSTsBxwAzq2tWyx0GfB44HHiY9MXiQklP5STaDEiLZ4DrSEn1cRU3p+Uk\ndQEnkeZ+Vs6Xra1Kz5PmrmxZKt8SeKb1zRlYki4BDgAmRsTThUPPkOZ69hWHZ4CNJW3WR53BrgvY\nAuiR9KqkV4G9ga9IeoX07bgT4gDwNFC+7LQQ2Do/7pT3xNnAWRFxXUT8ISJmA+cDJ+fjnRKHsmb1\n+xmgvNJ2GPAOhlBsConje4F9C6OO0Dlx2JP0+flE4fNzG+A8SY/kOi2LhZNHq0wefeomrfgCXr+s\nO4k0F6pt5MTxYOCTEfF48VhELCb90BbjsBlp/kktDt2kCc3FOtuTko37BrTxzTMX2Jk0ujQub/OA\nWcC4iHiEzogDpJXW5akZ2wOPQUe9J0aRvkAWrSb/buqgOKylif2+D9hcUnG0ahIpMf3tQLW/mQqJ\n4weASRGxtFSlI+JAmuv4EdZ8do4jLao6m7TgBVoZi6pXFHnr7A34HLACOIJ0q44rgL8AW1Tdtib2\n8TLSLVj2In0DrG0jC3Wm5X4fREqwfgH8Edi4dJ7FwETSKN49wF1V9+9Nxqa82roj4kCa87mSNML2\nQdKl2xeBwzspFsBVpMn8B5BGUQ4hzcf6TrvHgXRblnGkL1Orga/m/fc2s9+kxUfzgF1J02UWATOr\n7n8jcSBNrbue9KVqZ9b+/BzeTnFo5D1Rp/5aq61bGYvKg+XNG2n+yqOk21DcB+xSdZua3L/VpNGV\n8nZEqd63SN8kVwC3ANuWjo8g3S/yeVKicR3wrqr79yZjczuF5LGT4kBKmBbkfv4BOLJOnbaORf5l\neV7+ZbeclBydDmzU7nEgTdmo99nww2b2m7Q6eRbQS/oSeyUwqur+NxIH0heK8rHa/oR2ikOj74lS\n/UdYN3lsSSyUT2RmZmZmtkGe82hmZmZmDXPyaGZmZmYNc/JoZmZmZg1z8mhmZmZmDXPyaGZmZmYN\nc/JoZmZmZg1z8mhmZmZmDXPyaGZmZmYNc/JoZmZmZg1z8mhmZmZmDXPyaGZmZmYN+3/S8d1sTstZ\n3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113656048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "tz_counts[:10].plot(kind='barh', rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们还可以对这种数据进行更多的处理。比如a字段含有执行URL操作的浏览器、设备、应用程序的相关信息："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GoogleMaps/RochesterNY'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/20100101 Firefox/10.0.2'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mozilla/5.0 (Linux; U; Android 2.2.2; en-us; LG-P925/V10e Build/FRG83G) AppleWebKit/533.1 (KHTML, like Gecko) Version/4.0 Mobile Safari/533.1'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][51]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...\n",
       "1                               GoogleMaps/RochesterNY\n",
       "2    Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...\n",
       "3    Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...\n",
       "4    Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...\n",
       "Name: a, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将这些USER_AGENT字符串中的所有信息都解析出来是一件挺郁闷的工作。不过只要掌握了Python内置的字符串函数和正则表达式，就方便了。比如，我们可以把字符串的第一节（与浏览器大致对应）分离出来得到另一份用户行为摘要："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = Series([x.split()[0] for x in frame.a.dropna()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               Mozilla/5.0\n",
       "1    GoogleMaps/RochesterNY\n",
       "2               Mozilla/4.0\n",
       "3               Mozilla/5.0\n",
       "4               Mozilla/5.0\n",
       "dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mozilla/5.0                 2594\n",
       "Mozilla/4.0                  601\n",
       "GoogleMaps/RochesterNY       121\n",
       "Opera/9.80                    34\n",
       "TEST_INTERNET_AGENT           24\n",
       "GoogleProducer                21\n",
       "Mozilla/6.0                    5\n",
       "BlackBerry8520/5.0.0.681       4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.value_counts()[:8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，假设我们想按Windows和非Windows用户对时区统计信息进行分解。为了简单期间，我们假定只要agent字符串中含有“windows”就认为该用户是windows用户。由于有的agent缺失，所以先将他们从数据中移除："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cframe = frame[frame.a.notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其次根据a值计算出各行是否是windows："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "operating_system = np.where(cframe['a'].str.contains('Windows'), \n",
    "                            'Windows', 'Not Windows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Windows', 'Not Windows', 'Windows', 'Not Windows', 'Windows'], \n",
       "      dtype='<U11')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "operating_system[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来就可以根据时区和新得到的操作系统列表对数据进行分组了："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "by_tz_os = cframe.groupby(['tz', operating_system])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz                                         \n",
       "                                Not Windows    245\n",
       "                                Windows        276\n",
       "Africa/Cairo                    Windows          3\n",
       "Africa/Casablanca               Windows          1\n",
       "Africa/Ceuta                    Windows          2\n",
       "Africa/Johannesburg             Windows          1\n",
       "Africa/Lusaka                   Windows          1\n",
       "America/Anchorage               Not Windows      4\n",
       "                                Windows          1\n",
       "America/Argentina/Buenos_Aires  Not Windows      1\n",
       "America/Argentina/Cordoba       Windows          1\n",
       "America/Argentina/Mendoza       Windows          1\n",
       "America/Bogota                  Not Windows      1\n",
       "                                Windows          2\n",
       "America/Caracas                 Windows          1\n",
       "America/Chicago                 Not Windows    115\n",
       "                                Windows        285\n",
       "America/Chihuahua               Not Windows      1\n",
       "                                Windows          1\n",
       "America/Costa_Rica              Windows          1\n",
       "America/Denver                  Not Windows    132\n",
       "                                Windows         59\n",
       "America/Edmonton                Not Windows      2\n",
       "                                Windows          4\n",
       "America/Guayaquil               Not Windows      2\n",
       "America/Halifax                 Not Windows      1\n",
       "                                Windows          3\n",
       "America/Indianapolis            Not Windows      8\n",
       "                                Windows         12\n",
       "America/La_Paz                  Windows          1\n",
       "                                              ... \n",
       "Europe/Madrid                   Not Windows     16\n",
       "                                Windows         19\n",
       "Europe/Malta                    Windows          2\n",
       "Europe/Moscow                   Not Windows      1\n",
       "                                Windows          9\n",
       "Europe/Oslo                     Not Windows      2\n",
       "                                Windows          8\n",
       "Europe/Paris                    Not Windows      4\n",
       "                                Windows         10\n",
       "Europe/Prague                   Not Windows      3\n",
       "                                Windows          7\n",
       "Europe/Riga                     Not Windows      1\n",
       "                                Windows          1\n",
       "Europe/Rome                     Not Windows      8\n",
       "                                Windows         19\n",
       "Europe/Skopje                   Windows          1\n",
       "Europe/Sofia                    Windows          1\n",
       "Europe/Stockholm                Not Windows      2\n",
       "                                Windows         12\n",
       "Europe/Uzhgorod                 Windows          1\n",
       "Europe/Vienna                   Not Windows      3\n",
       "                                Windows          3\n",
       "Europe/Vilnius                  Windows          2\n",
       "Europe/Volgograd                Windows          1\n",
       "Europe/Warsaw                   Not Windows      1\n",
       "                                Windows         15\n",
       "Europe/Zurich                   Not Windows      4\n",
       "Pacific/Auckland                Not Windows      3\n",
       "                                Windows          8\n",
       "Pacific/Honolulu                Windows         36\n",
       "dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_tz_os.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后通过size对分组结果进行计数（类似于上面的value_counts函数），并利用unstack对计数结果进行重塑："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "agg_counts = by_tz_os.size().unstack().fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Not Windows</th>\n",
       "      <th>Windows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Cairo</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Casablanca</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Ceuta</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Johannesburg</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Lusaka</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Anchorage</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Buenos_Aires</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Cordoba</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Mendoza</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Not Windows  Windows\n",
       "tz                                                  \n",
       "                                      245.0    276.0\n",
       "Africa/Cairo                            0.0      3.0\n",
       "Africa/Casablanca                       0.0      1.0\n",
       "Africa/Ceuta                            0.0      2.0\n",
       "Africa/Johannesburg                     0.0      1.0\n",
       "Africa/Lusaka                           0.0      1.0\n",
       "America/Anchorage                       4.0      1.0\n",
       "America/Argentina/Buenos_Aires          1.0      0.0\n",
       "America/Argentina/Cordoba               0.0      1.0\n",
       "America/Argentina/Mendoza               0.0      1.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_counts[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后，我们来选取最常出现的时区。为了达到这个目的，根据agg_counts中的行数构造了一个简洁索引数组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "                                  521.0\n",
       "Africa/Cairo                        3.0\n",
       "Africa/Casablanca                   1.0\n",
       "Africa/Ceuta                        2.0\n",
       "Africa/Johannesburg                 1.0\n",
       "Africa/Lusaka                       1.0\n",
       "America/Anchorage                   5.0\n",
       "America/Argentina/Buenos_Aires      1.0\n",
       "America/Argentina/Cordoba           1.0\n",
       "America/Argentina/Mendoza           1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexer = agg_counts.sum(1)\n",
    "indexer[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "                                  24\n",
       "Africa/Cairo                      20\n",
       "Africa/Casablanca                 21\n",
       "Africa/Ceuta                      92\n",
       "Africa/Johannesburg               87\n",
       "Africa/Lusaka                     53\n",
       "America/Anchorage                 54\n",
       "America/Argentina/Buenos_Aires    57\n",
       "America/Argentina/Cordoba         26\n",
       "America/Argentina/Mendoza         55\n",
       "dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexer = agg_counts.sum(1).argsort()\n",
    "indexer[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后我通过take按照这个顺序截取了最后10行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "count_subset = agg_counts.take(indexer)[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Not Windows</th>\n",
       "      <th>Windows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>America/Sao_Paulo</th>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/Madrid</th>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific/Honolulu</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Tokyo</th>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/London</th>\n",
       "      <td>43.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Denver</th>\n",
       "      <td>132.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Los_Angeles</th>\n",
       "      <td>130.0</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Chicago</th>\n",
       "      <td>115.0</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/New_York</th>\n",
       "      <td>339.0</td>\n",
       "      <td>912.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Not Windows  Windows\n",
       "tz                                       \n",
       "America/Sao_Paulo           13.0     20.0\n",
       "Europe/Madrid               16.0     19.0\n",
       "Pacific/Honolulu             0.0     36.0\n",
       "Asia/Tokyo                   2.0     35.0\n",
       "Europe/London               43.0     31.0\n",
       "America/Denver             132.0     59.0\n",
       "America/Los_Angeles        130.0    252.0\n",
       "America/Chicago            115.0    285.0\n",
       "                           245.0    276.0\n",
       "America/New_York           339.0    912.0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_subset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里也可以生成一张条形图。我们使用stacked=True来生成一张堆积条形图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113c117b8>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0EDMzsx7lTU2tkLS1pHck3VhwKFOAnbq7U0ljJc3P3v9a0hJJiyW9LelFSX+S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\n7PyVKlysh8cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113c0c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_subset.plot(kind='barh', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于这张图中不太容易看清楚较小分组中windows用户的相对比例，因此我们可以将各行规范化为“总计为1”并重新绘图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "normed_subset = count_subset.div(count_subset.sum(1), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113cb4400>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xNDMza2Jdu3alc+fOlU9+MWuROnfuTNeuXXN6TCeeZmZmTaxnz56Ul5e3iCcU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\nk2XF2+wkbSTpakkvSpolaWQ/zhn0Z6gTT2taknYGTgZGAV8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113cc8668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normed_subset.plot(kind='barh', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MovieLens 1M数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个数据集是电影评分数据：包括电影评分，电影元数据（风格类型，年代）以及关于用户的人口统计学数据（年龄，邮编，性别，职业等）。\n",
    "\n",
    "MovieLens 1M数据集含有来自6000名用户对4000部电影的100万条评分数据。飞卫三个表：评分，用户信息，电影信息。这些数据都是dat文件格式，可以通过pandas.read_table将各个表分别读到一个pandas DataFrame对象中："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unames = ['user_id', 'gender', 'age', 'occupation', 'zip']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xu/anaconda/envs/py35/lib/python3.5/site-packages/ipykernel/__main__.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "users = pd.read_table('../datasets/movielens/users.dat', sep='::', header=None, names=unames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为sep='::'有点像是正则表达式，于是有了上面的错误。在这个[帖子](https://stackoverflow.com/questions/27301477/python-file-path-failing-in-pycharm-regex-confusion)找到了解决方法。\n",
    "\n",
    "Looks like on Python 2.7 Pandas just doesn't handle separators that look regexish. The initial \"error\" can be worked around by adding engine='python' as a named parameter in the call, as suggested in the warning.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "users = pd.read_table('../datasets/movielens/users.dat', sep='::', header=None, names=unames, engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rnames = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
    "ratings = pd.read_table('../datasets/movielens/ratings.dat', sep='::', header=None, names=rnames, engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mnames = ['movie_id', 'title', 'genres']\n",
    "movies = pd.read_table('../datasets/movielens/movies.dat', sep='::', header=None, names=mnames, engine='python')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "加载前几行验证一下数据加载工作是否顺利"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>48067</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>70072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>55117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>02460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>55455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id gender  age  occupation    zip\n",
       "0        1      F    1          10  48067\n",
       "1        2      M   56          16  70072\n",
       "2        3      M   25          15  55117\n",
       "3        4      M   45           7  02460\n",
       "4        5      M   25          20  55455"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "   user_id  movie_id  rating  timestamp\n",
       "0        1      1193       5  978300760\n",
       "1        1       661       3  978302109\n",
       "2        1       914       3  978301968\n",
       "3        1      3408       4  978300275\n",
       "4        1      2355       5  978824291"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                               title                        genres\n",
       "0         1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1         2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2         3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3         4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4         5  Father of the Bride Part II (1995)                        Comedy"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意，年龄和职业是以编码形式给出的，它们的具体含义请参考改数据集的REAMDE文件。分析散布在三个表中的数据不是一件轻松的事情。假设我们想要根据性别和年龄来计算某部电影的平均得分，如果将所有的数据都合并到一个表中的话，问题就简单多了。我们先用pandas的merge函数将ratings和users合并到一起，然后再将movies也合并进去。pandas会根据列名的重叠情况推断出哪些列是合并（或连接）键："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.merge(pd.merge(ratings, users), movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978298413</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>70072</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>1193</td>\n",
       "      <td>4</td>\n",
       "      <td>978220179</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>32793</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1193</td>\n",
       "      <td>4</td>\n",
       "      <td>978199279</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>22903</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978158471</td>\n",
       "      <td>M</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>95350</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  rating  timestamp gender  age  occupation    zip  \\\n",
       "0        1      1193       5  978300760      F    1          10  48067   \n",
       "1        2      1193       5  978298413      M   56          16  70072   \n",
       "2       12      1193       4  978220179      M   25          12  32793   \n",
       "3       15      1193       4  978199279      M   25           7  22903   \n",
       "4       17      1193       5  978158471      M   50           1  95350   \n",
       "\n",
       "                                    title genres  \n",
       "0  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "1  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "2  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "3  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "4  One Flew Over the Cuckoo's Nest (1975)  Drama  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                                            1\n",
       "movie_id                                        1193\n",
       "rating                                             5\n",
       "timestamp                                  978300760\n",
       "gender                                             F\n",
       "age                                                1\n",
       "occupation                                        10\n",
       "zip                                            48067\n",
       "title         One Flew Over the Cuckoo's Nest (1975)\n",
       "genres                                         Drama\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ix[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，只要稍微熟悉一下pandas，就能轻松地根据任意个用户或电影属性对评分数据进行聚合操作了。为了按性别计算每部电影的平均得分，我们可以使用pivot_table方法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mean_ratings = data.pivot_table('rating', index='title',\n",
    "                                columns='gender', aggfunc='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$1,000,000 Duck (1971)</th>\n",
       "      <td>3.375000</td>\n",
       "      <td>2.761905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Night Mother (1986)</th>\n",
       "      <td>3.388889</td>\n",
       "      <td>3.352941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>2.675676</td>\n",
       "      <td>2.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>2.793478</td>\n",
       "      <td>2.962085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...And Justice for All (1979)</th>\n",
       "      <td>3.828571</td>\n",
       "      <td>3.689024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                F         M\n",
       "title                                            \n",
       "$1,000,000 Duck (1971)         3.375000  2.761905\n",
       "'Night Mother (1986)           3.388889  3.352941\n",
       "'Til There Was You (1997)      2.675676  2.733333\n",
       "'burbs, The (1989)             2.793478  2.962085\n",
       "...And Justice for All (1979)  3.828571  3.689024"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该操作产生了另一个DataFrame，其内容为电影平均得分，行标为电影名称，列表为性别。现在，我们打算过滤掉评分数据不够250条的电影（这个数字可以自己设定）。为了达到这个目的，我们先对title进行分组，然后利用size()得到一个含有各电影分组大小的Series对象："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ratings_by_title = data.groupby('title').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "$1,000,000 Duck (1971)                37\n",
       "'Night Mother (1986)                  70\n",
       "'Til There Was You (1997)             52\n",
       "'burbs, The (1989)                   303\n",
       "...And Justice for All (1979)        199\n",
       "1-900 (1994)                           2\n",
       "10 Things I Hate About You (1999)    700\n",
       "101 Dalmatians (1961)                565\n",
       "101 Dalmatians (1996)                364\n",
       "12 Angry Men (1957)                  616\n",
       "dtype: int64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_by_title[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "active_titles = ratings_by_title.index[ratings_by_title >= 250]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([''burbs, The (1989)', '10 Things I Hate About You (1999)',\n",
       "       '101 Dalmatians (1961)', '101 Dalmatians (1996)', '12 Angry Men (1957)',\n",
       "       '13th Warrior, The (1999)', '2 Days in the Valley (1996)',\n",
       "       '20,000 Leagues Under the Sea (1954)', '2001: A Space Odyssey (1968)',\n",
       "       '2010 (1984)',\n",
       "       ...\n",
       "       'X-Men (2000)', 'Year of Living Dangerously (1982)',\n",
       "       'Yellow Submarine (1968)', 'You've Got Mail (1998)',\n",
       "       'Young Frankenstein (1974)', 'Young Guns (1988)',\n",
       "       'Young Guns II (1990)', 'Young Sherlock Holmes (1985)',\n",
       "       'Zero Effect (1998)', 'eXistenZ (1999)'],\n",
       "      dtype='object', name='title', length=1216)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>2.793478</td>\n",
       "      <td>2.962085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 Things I Hate About You (1999)</th>\n",
       "      <td>3.646552</td>\n",
       "      <td>3.311966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1961)</th>\n",
       "      <td>3.791444</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>3.240000</td>\n",
       "      <td>2.911215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.184397</td>\n",
       "      <td>4.328421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                    F         M\n",
       "title                                                \n",
       "'burbs, The (1989)                 2.793478  2.962085\n",
       "10 Things I Hate About You (1999)  3.646552  3.311966\n",
       "101 Dalmatians (1961)              3.791444  3.500000\n",
       "101 Dalmatians (1996)              3.240000  2.911215\n",
       "12 Angry Men (1957)                4.184397  4.328421"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings = mean_ratings.ix[active_titles]\n",
    "mean_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Close Shave, A (1995)</th>\n",
       "      <td>4.644444</td>\n",
       "      <td>4.473795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wrong Trousers, The (1993)</th>\n",
       "      <td>4.588235</td>\n",
       "      <td>4.478261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)</th>\n",
       "      <td>4.572650</td>\n",
       "      <td>4.464589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wallace &amp; Gromit: The Best of Aardman Animation (1996)</th>\n",
       "      <td>4.563107</td>\n",
       "      <td>4.385075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schindler's List (1993)</th>\n",
       "      <td>4.562602</td>\n",
       "      <td>4.491415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shawshank Redemption, The (1994)</th>\n",
       "      <td>4.539075</td>\n",
       "      <td>4.560625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grand Day Out, A (1992)</th>\n",
       "      <td>4.537879</td>\n",
       "      <td>4.293255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>To Kill a Mockingbird (1962)</th>\n",
       "      <td>4.536667</td>\n",
       "      <td>4.372611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Creature Comforts (1990)</th>\n",
       "      <td>4.513889</td>\n",
       "      <td>4.272277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Usual Suspects, The (1995)</th>\n",
       "      <td>4.513317</td>\n",
       "      <td>4.518248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                                     F         M\n",
       "title                                                                 \n",
       "Close Shave, A (1995)                               4.644444  4.473795\n",
       "Wrong Trousers, The (1993)                          4.588235  4.478261\n",
       "Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)       4.572650  4.464589\n",
       "Wallace & Gromit: The Best of Aardman Animation...  4.563107  4.385075\n",
       "Schindler's List (1993)                             4.562602  4.491415\n",
       "Shawshank Redemption, The (1994)                    4.539075  4.560625\n",
       "Grand Day Out, A (1992)                             4.537879  4.293255\n",
       "To Kill a Mockingbird (1962)                        4.536667  4.372611\n",
       "Creature Comforts (1990)                            4.513889  4.272277\n",
       "Usual Suspects, The (1995)                          4.513317  4.518248"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_female_ratings = mean_ratings.sort_values(by='F', ascending=False)\n",
    "top_female_ratings[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算评分分歧\n",
    "\n",
    "假设我们想要找出男性和女性观众分歧最大的电影。一个办法是给mean_ratings加上一个用于存放平均得分之差的列，并对其进行排序："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mean_ratings['diff'] = mean_ratings['M'] - mean_ratings['F']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按‘diff’排序即可得到分歧最大且女性观众更喜欢的电影："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dirty Dancing (1987)</th>\n",
       "      <td>3.790378</td>\n",
       "      <td>2.959596</td>\n",
       "      <td>-0.830782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumpin' Jack Flash (1986)</th>\n",
       "      <td>3.254717</td>\n",
       "      <td>2.578358</td>\n",
       "      <td>-0.676359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grease (1978)</th>\n",
       "      <td>3.975265</td>\n",
       "      <td>3.367041</td>\n",
       "      <td>-0.608224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Little Women (1994)</th>\n",
       "      <td>3.870588</td>\n",
       "      <td>3.321739</td>\n",
       "      <td>-0.548849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steel Magnolias (1989)</th>\n",
       "      <td>3.901734</td>\n",
       "      <td>3.365957</td>\n",
       "      <td>-0.535777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anastasia (1997)</th>\n",
       "      <td>3.800000</td>\n",
       "      <td>3.281609</td>\n",
       "      <td>-0.518391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rocky Horror Picture Show, The (1975)</th>\n",
       "      <td>3.673016</td>\n",
       "      <td>3.160131</td>\n",
       "      <td>-0.512885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Color Purple, The (1985)</th>\n",
       "      <td>4.158192</td>\n",
       "      <td>3.659341</td>\n",
       "      <td>-0.498851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age of Innocence, The (1993)</th>\n",
       "      <td>3.827068</td>\n",
       "      <td>3.339506</td>\n",
       "      <td>-0.487561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free Willy (1993)</th>\n",
       "      <td>2.921348</td>\n",
       "      <td>2.438776</td>\n",
       "      <td>-0.482573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>French Kiss (1995)</th>\n",
       "      <td>3.535714</td>\n",
       "      <td>3.056962</td>\n",
       "      <td>-0.478752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Little Shop of Horrors, The (1960)</th>\n",
       "      <td>3.650000</td>\n",
       "      <td>3.179688</td>\n",
       "      <td>-0.470312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guys and Dolls (1955)</th>\n",
       "      <td>4.051724</td>\n",
       "      <td>3.583333</td>\n",
       "      <td>-0.468391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mary Poppins (1964)</th>\n",
       "      <td>4.197740</td>\n",
       "      <td>3.730594</td>\n",
       "      <td>-0.467147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patch Adams (1998)</th>\n",
       "      <td>3.473282</td>\n",
       "      <td>3.008746</td>\n",
       "      <td>-0.464536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                        F         M      diff\n",
       "title                                                              \n",
       "Dirty Dancing (1987)                   3.790378  2.959596 -0.830782\n",
       "Jumpin' Jack Flash (1986)              3.254717  2.578358 -0.676359\n",
       "Grease (1978)                          3.975265  3.367041 -0.608224\n",
       "Little Women (1994)                    3.870588  3.321739 -0.548849\n",
       "Steel Magnolias (1989)                 3.901734  3.365957 -0.535777\n",
       "Anastasia (1997)                       3.800000  3.281609 -0.518391\n",
       "Rocky Horror Picture Show, The (1975)  3.673016  3.160131 -0.512885\n",
       "Color Purple, The (1985)               4.158192  3.659341 -0.498851\n",
       "Age of Innocence, The (1993)           3.827068  3.339506 -0.487561\n",
       "Free Willy (1993)                      2.921348  2.438776 -0.482573\n",
       "French Kiss (1995)                     3.535714  3.056962 -0.478752\n",
       "Little Shop of Horrors, The (1960)     3.650000  3.179688 -0.470312\n",
       "Guys and Dolls (1955)                  4.051724  3.583333 -0.468391\n",
       "Mary Poppins (1964)                    4.197740  3.730594 -0.467147\n",
       "Patch Adams (1998)                     3.473282  3.008746 -0.464536"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_by_diff = mean_ratings.sort_values(by='diff')\n",
    "sorted_by_diff[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对行反序，并取出前15行，得到的则是男性更喜欢的电影"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Good, The Bad and The Ugly, The (1966)</th>\n",
       "      <td>3.494949</td>\n",
       "      <td>4.221300</td>\n",
       "      <td>0.726351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky Fried Movie, The (1977)</th>\n",
       "      <td>2.878788</td>\n",
       "      <td>3.555147</td>\n",
       "      <td>0.676359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dumb &amp; Dumber (1994)</th>\n",
       "      <td>2.697987</td>\n",
       "      <td>3.336595</td>\n",
       "      <td>0.638608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longest Day, The (1962)</th>\n",
       "      <td>3.411765</td>\n",
       "      <td>4.031447</td>\n",
       "      <td>0.619682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cable Guy, The (1996)</th>\n",
       "      <td>2.250000</td>\n",
       "      <td>2.863787</td>\n",
       "      <td>0.613787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evil Dead II (Dead By Dawn) (1987)</th>\n",
       "      <td>3.297297</td>\n",
       "      <td>3.909283</td>\n",
       "      <td>0.611985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hidden, The (1987)</th>\n",
       "      <td>3.137931</td>\n",
       "      <td>3.745098</td>\n",
       "      <td>0.607167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rocky III (1982)</th>\n",
       "      <td>2.361702</td>\n",
       "      <td>2.943503</td>\n",
       "      <td>0.581801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caddyshack (1980)</th>\n",
       "      <td>3.396135</td>\n",
       "      <td>3.969737</td>\n",
       "      <td>0.573602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>For a Few Dollars More (1965)</th>\n",
       "      <td>3.409091</td>\n",
       "      <td>3.953795</td>\n",
       "      <td>0.544704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porky's (1981)</th>\n",
       "      <td>2.296875</td>\n",
       "      <td>2.836364</td>\n",
       "      <td>0.539489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animal House (1978)</th>\n",
       "      <td>3.628906</td>\n",
       "      <td>4.167192</td>\n",
       "      <td>0.538286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exorcist, The (1973)</th>\n",
       "      <td>3.537634</td>\n",
       "      <td>4.067239</td>\n",
       "      <td>0.529605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fright Night (1985)</th>\n",
       "      <td>2.973684</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>0.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barb Wire (1996)</th>\n",
       "      <td>1.585366</td>\n",
       "      <td>2.100386</td>\n",
       "      <td>0.515020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                         F         M      diff\n",
       "title                                                               \n",
       "Good, The Bad and The Ugly, The (1966)  3.494949  4.221300  0.726351\n",
       "Kentucky Fried Movie, The (1977)        2.878788  3.555147  0.676359\n",
       "Dumb & Dumber (1994)                    2.697987  3.336595  0.638608\n",
       "Longest Day, The (1962)                 3.411765  4.031447  0.619682\n",
       "Cable Guy, The (1996)                   2.250000  2.863787  0.613787\n",
       "Evil Dead II (Dead By Dawn) (1987)      3.297297  3.909283  0.611985\n",
       "Hidden, The (1987)                      3.137931  3.745098  0.607167\n",
       "Rocky III (1982)                        2.361702  2.943503  0.581801\n",
       "Caddyshack (1980)                       3.396135  3.969737  0.573602\n",
       "For a Few Dollars More (1965)           3.409091  3.953795  0.544704\n",
       "Porky's (1981)                          2.296875  2.836364  0.539489\n",
       "Animal House (1978)                     3.628906  4.167192  0.538286\n",
       "Exorcist, The (1973)                    3.537634  4.067239  0.529605\n",
       "Fright Night (1985)                     2.973684  3.500000  0.526316\n",
       "Barb Wire (1996)                        1.585366  2.100386  0.515020"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_by_diff[::-1][:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果只是想要找出分歧最大的电影（不考虑性别因素），则可以计算得分数据的方差或标准差："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 根据电影名称分组的得分数据的标准差\n",
    "rating_std_by_title = data.groupby('title')['rating'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 根据active_titles进行过滤\n",
    "rating_std_by_title = rating_std_by_title.ix[active_titles]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Dumb & Dumber (1994)                     1.321333\n",
       "Blair Witch Project, The (1999)          1.316368\n",
       "Natural Born Killers (1994)              1.307198\n",
       "Tank Girl (1995)                         1.277695\n",
       "Rocky Horror Picture Show, The (1975)    1.260177\n",
       "Eyes Wide Shut (1999)                    1.259624\n",
       "Evita (1996)                             1.253631\n",
       "Billy Madison (1995)                     1.249970\n",
       "Fear and Loathing in Las Vegas (1998)    1.246408\n",
       "Bicentennial Man (1999)                  1.245533\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_std_by_title.sort_values(ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                               title                        genres\n",
       "0         1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1         2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2         3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3         4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4         5  Father of the Bride Part II (1995)                        Comedy"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里我们注意到，电影分类是以竖线`|`分割的字符串形式给出的。如果想对电影分类进行分析的话，就需要先将其转换成更有用的形式才行。我将在本书后续章节中讲到这种转换处理，到时候还会再用到这个数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1880-2010年间全美婴儿姓名"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个数据时从1880年到2010年婴儿名字频率数据。这个数据集可以用来做很多事，例如：\n",
    "\n",
    "- 计算指定名字的年度比例\n",
    "- 计算某个名字的相对排名\n",
    "- 计算各年度最流行的名字，以及增长或减少最快的名字\n",
    "- 分析名字趋势：元音、辅音、长度、总体多样性、拼写变化、首尾字母等\n",
    "- 分析外源性趋势：圣经中的名字、名人、人口结构变化等\n",
    "\n",
    "在babynames文件夹下可以看到这些文件是按年度来编号的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mary,F,7065\r",
      "\r\n",
      "Anna,F,2604\r",
      "\r\n",
      "Emma,F,2003\r",
      "\r\n",
      "Elizabeth,F,1939\r",
      "\r\n",
      "Minnie,F,1746\r",
      "\r\n",
      "Margaret,F,1578\r",
      "\r\n",
      "Ida,F,1472\r",
      "\r\n",
      "Alice,F,1414\r",
      "\r\n",
      "Bertha,F,1320\r",
      "\r\n",
      "Sarah,F,1288\r",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!head -n 10 ../datasets/babynames/yob1880.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于这是一个非常标准的以逗号隔开的格式（即CSV文件），所以可以用pandas.read_csv将其加载到DataFrame中："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names1880 = pd.read_csv('../datasets/babynames/yob1880.txt', names=['names', 'sex', 'births'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>names</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       names sex  births\n",
       "0       Mary   F    7065\n",
       "1       Anna   F    2604\n",
       "2       Emma   F    2003\n",
       "3  Elizabeth   F    1939\n",
       "4     Minnie   F    1746"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这些文件中仅含有当年出现超过5次的名字。为了简单期间，我们可以用births列的sex分组小计，表示该年度的births总计："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "F     90993\n",
       "M    110493\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880.groupby('sex').births.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于该数据集按年度被分割成了多个文件，所以第一件事情就是要将所有数据都组装到一个DataFrame里面，并加上一个year字段。使用pandas.concat可以做到："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 2010是最后一个有效统计年度\n",
    "years = range(1880, 2011)\n",
    "\n",
    "pieces = []\n",
    "columns = ['name', 'sex', 'births']\n",
    "\n",
    "for year in years:\n",
    "    path = '../datasets/babynames/yob%d.txt' % year\n",
    "    frame = pd.read_csv(path, names=columns)\n",
    "    \n",
    "    frame['year'] = year\n",
    "    pieces.append(frame)\n",
    "    \n",
    "# 将所有数据整合到单个DataFrame中\n",
    "names = pd.concat(pieces, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里要注意几件事。\n",
    "\n",
    "- 第一，concat默认是按行将多个DataFrame组合到一起的；\n",
    "- 第二，必须指定ignore_index=True，因为我们不希望保留read_csv所返回的原始行号。\n",
    "\n",
    "现在我们得到了一个非常大的DataFrame，它含有全部的名字数据。现在names这个DataFrame看上去是："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births  year\n",
       "0       Mary   F    7065  1880\n",
       "1       Anna   F    2604  1880\n",
       "2       Emma   F    2003  1880\n",
       "3  Elizabeth   F    1939  1880\n",
       "4     Minnie   F    1746  1880"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有了这些数据后，我们就可以利用groupby或pivot_table在year和sex界别上对其进行聚合了："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total_births = names.pivot_table('births', index='year',\n",
    "                                columns='sex', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1896468</td>\n",
       "      <td>2050234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1916888</td>\n",
       "      <td>2069242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1883645</td>\n",
       "      <td>2032310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1827643</td>\n",
       "      <td>1973359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1759010</td>\n",
       "      <td>1898382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex         F        M\n",
       "year                  \n",
       "2006  1896468  2050234\n",
       "2007  1916888  2069242\n",
       "2008  1883645  2032310\n",
       "2009  1827643  1973359\n",
       "2010  1759010  1898382"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1146e5ef0>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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K3HTD2RnbE0k2UBzjpA1wv0lgIy5HvgS2lHG0IbMFW/CpMbaQT3SQbElsASKcv/lEpKZL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\nREREJOwoQREREZGw8/8B083WWBRidSAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11468a630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_births.plot(title='Total births by sex and year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们来插入一个prop列，用于存放指定名字的婴儿数相对于总出生数的比列。prop值为0.02表示每100名婴儿中有2名去了当前这个名字。因此，我们先按year和sex分组，然后再将新列加到各个分组上："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def add_prop(group):\n",
    "    # 整数出发回向下圆整\n",
    "    # births = group.births.astype(float)\n",
    "    births = group.births\n",
    "    \n",
    "    group['prop'] = births / births.sum()\n",
    "    return group\n",
    "\n",
    "names = names.groupby(['year', 'sex']).apply(add_prop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意：由于births是整数，所以我们在计算分式时必须将分子或分母转换成浮点数（除非使用的是python3）。所以上面我们可以不加`births = group.births.astype(float)`这一句代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births  year      prop\n",
       "0       Mary   F    7065  1880  0.077643\n",
       "1       Anna   F    2604  1880  0.028618\n",
       "2       Emma   F    2003  1880  0.022013\n",
       "3  Elizabeth   F    1939  1880  0.021309\n",
       "4     Minnie   F    1746  1880  0.019188"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在执行这样的分组处理时，一般都应该做一些有效性检查，比如验证所有分组的prop的综合是否为1。由于这是一个浮点型数据，所以我们应该用np.allclose来检查这个分组总计值是否够近似于（可能不会精确等于）1："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(names.groupby(['year', 'sex']).prop.sum(), 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这样就算完活了。为了便于实现进一步的分析，我们需要取出该数据的一个子集：每对sex/year组合的前1000个名字。这又是一个分组操作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_top1000(group):\n",
    "    return group.sort_values(by='births', ascending=False)[:1000]\n",
    "\n",
    "grouped = names.groupby(['year', 'sex'])\n",
    "top1000 = grouped.apply(get_top1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果喜欢DIY的话，也可以这样："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pieces =[]\n",
    "for year, group in names.groupby(['year', 'sex']):\n",
    "    pieces.append(group.sort_values(by='births', ascending=False)[:1000])\n",
    "    \n",
    "top1000 = pd.concat(pieces, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.017342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.016177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.015540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births  year      prop\n",
       "0       Mary   F    7065  1880  0.077643\n",
       "1       Anna   F    2604  1880  0.028618\n",
       "2       Emma   F    2003  1880  0.022013\n",
       "3  Elizabeth   F    1939  1880  0.021309\n",
       "4     Minnie   F    1746  1880  0.019188\n",
       "5   Margaret   F    1578  1880  0.017342\n",
       "6        Ida   F    1472  1880  0.016177\n",
       "7      Alice   F    1414  1880  0.015540\n",
       "8     Bertha   F    1320  1880  0.014507\n",
       "9      Sarah   F    1288  1880  0.014155"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来的数据分析工作就针对这个top1000数据集了\n",
    "\n",
    "## 分析命名趋势\n",
    "\n",
    "有了完整的数据集和刚才生成的top1000数据集，我们就可以开始分析各种命名趋势了。首先将前1000个名字分为男女两个部分：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "boys = top1000[top1000.sex=='M']\n",
    "girls = top1000[top1000.sex=='F']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这是两个简单的时间序列，只需要稍作整理即可绘制出相应的图标（比如每年叫做John和Mary的婴儿数）。我们先生成一张按year和name统计的总出生数透视表："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>name</th>\n",
       "      <th>Aaden</th>\n",
       "      <th>Aaliyah</th>\n",
       "      <th>Aarav</th>\n",
       "      <th>Aaron</th>\n",
       "      <th>Aarush</th>\n",
       "      <th>Ab</th>\n",
       "      <th>Abagail</th>\n",
       "      <th>Abb</th>\n",
       "      <th>Abbey</th>\n",
       "      <th>Abbie</th>\n",
       "      <th>...</th>\n",
       "      <th>Zoa</th>\n",
       "      <th>Zoe</th>\n",
       "      <th>Zoey</th>\n",
       "      <th>Zoie</th>\n",
       "      <th>Zola</th>\n",
       "      <th>Zollie</th>\n",
       "      <th>Zona</th>\n",
       "      <th>Zora</th>\n",
       "      <th>Zula</th>\n",
       "      <th>Zuri</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 6868 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "name  Aaden  Aaliyah  Aarav  Aaron  Aarush  Ab  Abagail  Abb  Abbey  Abbie  \\\n",
       "year                                                                         \n",
       "1880    NaN      NaN    NaN  102.0     NaN NaN      NaN  NaN    NaN   71.0   \n",
       "1881    NaN      NaN    NaN   94.0     NaN NaN      NaN  NaN    NaN   81.0   \n",
       "1882    NaN      NaN    NaN   85.0     NaN NaN      NaN  NaN    NaN   80.0   \n",
       "1883    NaN      NaN    NaN  105.0     NaN NaN      NaN  NaN    NaN   79.0   \n",
       "1884    NaN      NaN    NaN   97.0     NaN NaN      NaN  NaN    NaN   98.0   \n",
       "\n",
       "name  ...    Zoa   Zoe  Zoey  Zoie  Zola  Zollie  Zona  Zora  Zula  Zuri  \n",
       "year  ...                                                                 \n",
       "1880  ...    8.0  23.0   NaN   NaN   7.0     NaN   8.0  28.0  27.0   NaN  \n",
       "1881  ...    NaN  22.0   NaN   NaN  10.0     NaN   9.0  21.0  27.0   NaN  \n",
       "1882  ...    8.0  25.0   NaN   NaN   9.0     NaN  17.0  32.0  21.0   NaN  \n",
       "1883  ...    NaN  23.0   NaN   NaN  10.0     NaN  11.0  35.0  25.0   NaN  \n",
       "1884  ...   13.0  31.0   NaN   NaN  14.0     6.0   8.0  58.0  27.0   NaN  \n",
       "\n",
       "[5 rows x 6868 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births = top1000.pivot_table('births', index='year', \n",
    "                                   columns='name', aggfunc=sum)\n",
    "\n",
    "total_births[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "subset = total_births[['John', 'Harry', 'Mary', 'Marilyn']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x1140cbd30>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x12c132e80>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x120a96518>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1217b7630>], dtype=object)"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jKrlr2dKX2dOa5yJSmPr168fSpUtp3rw5Dz74IL1796ZVq1ZUr16d7t2L96ra\naf1JNbO5Zpadw+PhpJheZrbQzNaY2TtmVi/lGBXMbIiZLTGzlWY2wsx2S4mpbmbPmdlyM1tmZkPN\nrHJKTB0zG21mq81skZn1NTP9iyAiIlLEPv/cl/LSkHbJi5kvqzZypFeVFxEpiMSc78Sc8FatWvHW\nW29Rs2ZNunfvTv/+/TnyyCP5+OOPNw17T5eZYTkUUMlte1FJN5ltCtRKepwIBOAlADO7FbgGuBxo\nBqwGxppZ+aRjDATaAG2BY4DawMiU8zwPNABaRbHHAI8ldkZJ+Bh8znxzoBPQGeiV5vWIiIhImkaO\nhF12gWOOibslsr3r1An++MPXbBcRKYiVK1cCUKVKlU3bjj/+eD766CNWrVrFsmXLeOWVV6hfv/5m\nr+vevTsbN26kRo0am23v1KkTGzdu3KyK+8aNG3nooYf+cu45c+YwbNiwwrycPKWVnIcQloYQfk48\ngNOA70II46OQrkDvEMKbIYTpQEc8+T4DwMyqABcD3UIIH4YQvgAuAlqaWbMopgHQGrgkhPB5CGEi\ncC1wvpnVis7TGjgA6BBCmBZCGAvcBVxtZipyJyIiUkRC8OT8jDOgnP7iSj5q1YJ//hP69oVVq+Ju\njYgUR5MnT6Zy5coF7hUvTgo8DNzMdgA6AMOi5/vgvenvJmJCCCuAT4EW0aameG93cswsYH5STHNg\nWZS4J4zDe+iPSIqZFkJYkhQzFqgKHFTQaxIREZG8TZvma29rSLtsqQcegF9+8artIiJbatSoUVx7\n7bU8//zzdOjQgTKloMjJ1lzhmXgy/HT0vBaeQC9OiVsc7QPIANZHSXtuMbWAn5N3hhA2Ar+mxOR0\nHpJiREREpJCNHOlrardqFXdLpLjYd1+45x4YPBgmTIi7NSJSXNx00028+OKLXHbZZfTv3z/u5mwT\nWzMg7WLgvyGERYXVmG2lW7duVK1adbNt7dq1o127djG1SEREpHgYORJOOw3Kl88/ViTh2mvhxRd9\nebUvv4Qdd4y7RSKyvZszZ07cTSiQzMxMMjMzN9u2fPnyLXptgZJzM6sLnEA0lzyyCDC8dzy5VzsD\n+CIppryZVUnpPc+I9iViUqu3lwVqpMQcntKsjKR9eRowYACNGzfOL0xERKREmTbN5/62agUXXghR\n4dstNmsWfPMN3H130bRPSq6yZWHYMGjUCHr2hPvui7tFIiJFI6dO36lTp9KkSZN8X1vQYe0X4wn4\nmMSGEMKiEWFeAAAgAElEQVRcPDHeNNAtKgB3BDAx2jQF2JASUx+oC0yKNk0CqplZo6TztcIT/0+T\nYhqaWc2kmJOA5cCMAl6TiIhIibRhgydDTZrAuHFw0UX+/TvvpHeckSOhcmVo3bpo2ikl24EHwl13\nQb9+MHVq3K0REdn+pJ2cmy/01hn4TwghO2X3QOBOMzvNzBoCzwALgNdgU4G4YUB/MzvOzJoATwIT\nQgiTo5gsvLjbE2Z2uJm1BB4GMpOG0L+NJ+HDzewQM2sN9AYGhxD+SPeaRERESqqsLGjZEu68E264\nAebOhYkTPck+6SQ4+WTvUd8SI0fCKadAxYpF22YpuW69FQ4+2Nc//0P/sYmIbKYgPecnAHWAp1J3\nhBD64on0Y3gvd0Xg5BDC+qSwbsCbwAjgA2AhvuZ5svZAFl6l/U3gI+CKpPNkA6cCG/Fe+WeA/wDd\nC3A9IiIiJc7GjdC/vw8jXrYMPv4Y+vTxub4tWvjzkSO98vphh8Gll8LChbkfb+5c7+1UlXbZGjvs\nAE8+CdOnw/33x90aEZHtS9pzzkMI7wC5zlILIfQAeuSx/3d83fJr84j5Dbggn3b8gCfoIiIikmT2\nbB+6/vHH0LUr3HsvVKq0eYwZnHUWnHoqPPaYzwPOzITmzaF2bdh9d38kvh87FipU8J5zka3RuDHc\nfDP07u2fwQMPjLtFIiXHzJkz425CqVRY7/vWVGsXERGR7cw330CzZpCRAR98AMcem3d8+fJeSfvC\nC+Hhh71Hc948mDTJe9LXrv0z9vTTYeedi7T5Ukp07w6vvOLD2ydMSL84oYhsrmbNmlSqVIkLLsiz\nf1OKUKVKlahZs2b+gXlQci4iIlKC9OgBu+0GX32VXiJdrZoX60oWAqxYAT/95I+DDy7UpkoptuOO\nXr396KPhoYe8HoKIFFzdunWZOXMmS5YsibsppVbNmjWpW7fuVh1DybmIiEgJMX06jBgBTzxROD3c\nZlC1qj8OOGDrjyeSrGVLn3Zx002wfr0XizOLu1UixVfdunW3OjmUeCk5FxERKSF694a99oKOHeNu\niciWefBBv5F0++1ecPCpp3wlARGR0qig65yLiIjIdmTGDHj5ZbjjDp9HLlIclCkDvXr5ygFjxsCR\nR/rKACIipZGScxERkRKgd2+oUwc6d467JSLpO+ss+OQTWLUKmjaFd9+Nu0UiItte2sm5mdU2s+Fm\ntsTM1pjZV2bWOCWml5ktjPa/Y2b1UvZXMLMh0TFWmtkIM9stJaa6mT1nZsvNbJmZDTWzyikxdcxs\ntJmtNrNFZtbXzHTDQURESpWZM+HFF31osHrNpbg6+GD47DNo0gRat4aBA70ooYhIaZFWImtm1YAJ\nwO9Aa6ABcCOwLCnmVuAa4HKgGbAaGGtmyf8uDATaAG2BY4DawMiU0z0fHb9VFHsM8FjSecoAY/B5\n882BTkBnoFc61yQiIlLc3X037LGHr20uUpzVqOHD26+/Hrp1g7Zt4fHHYfx4WLo07taJiBStdAvC\n3QbMDyFcmrRtXkpMV6B3COFNADPrCCwGzgBeMrMqwMXA+SGED6OYi4CZZtYshDDZzBrgyX+TEMIX\nUcy1wGgzuymEsCjafwBwfAhhCTDNzO4C+phZjxDChjSvTUREpNiZNQteeMHXKK9QIe7WiGy9cuXg\ngQegcWOfj/7aa5Cd7ft22w0OPBAaNIBDD4X27QtnZQIRke1BukPATwM+N7OXzGyxmU01s02Jupnt\nA9QCNs0UCiGsAD4FWkSbmuI3BZJjZgHzk2KaA8sSiXlkHBCAI5JipkWJecJYoCpwUJrXJSIiUizd\nfTfsvjtcckncLREpXO3bQ1YWrFkDX3/tUze6dIFdd/We9GuugX32gfvvh9Wr426tiMjWS7fnfF+g\nC/AgcA8+bH2Qmf0eQhiOJ+YB7ylPtjjaB5ABrI+S9txiagE/J+8MIWw0s19TYnI6T2LfV+ldmoiI\nSPHy7bfw/PM+N1e95lJSVagADRv6I9mCBXDvvXDXXdC/P9x2G1x5JVSsGE87RUS2VrrJeRlgcgjh\nruj5V2Z2MHAlMLxQW1aEunXrRtWqVTfb1q5dO9q1axdTi0RECsfKlTB2LDRqBPvtF3drpKjdey9k\nZMBll8XdEpFtb8894ZFH4JZbfATJzTdDv36+nOBll+mGlYjEIzMzk8zMzM22LV++fItem25y/hMw\nM2XbTOCs6PtFgOG948m92hnAF0kx5c2sSkrveUa0LxGTWr29LFAjJebwlLZkJO3L1YABA2jcuHFe\nISIixUYIMGkSDB0KL73kwzvNfGmiG2+EFi3yP4YUP999B88+Cw8+CDvuGHdrROKz997+++/2232O\neteucN993tNetao/qlT58/vq1eGMMzRXXUSKRk6dvlOnTqVJkyb5vjbdOecTgPop2+oTFYULIczF\nE+NWiZ1RAbgjgInRpinAhpSY+kBdYFK0aRJQzcwaJZ2nFZ74f5oU09DMaibFnAQsB2akeV0iItvM\nqlUwciR07AgtW/o/ki++CPPnp7ds0C+/eGJ20EF+nPfeg1tv9aHOjz4K06bBkUf6vlGjYOPGorsm\n2fbuucfn3l5+edwtEdk+7LcfPP00zJjhNycrV/YK7198Aa+/DkOG+O/Ijh2hU6e4Wysi8lfp9pwP\nACaY2e3AS3jSfSmQPKBuIHCnmc0Gvgd6AwuA18ALxJnZMKC/mS0DVgKDgAkhhMlRTJaZjQWeMLMu\nQHngYSAzqtQO8DaehA+Plm/bPTrX4BDCH2lel4hIkfr5Z3jjDXj1VXjnHfj9d1/T98AD4c03YdAg\nj9tjD+/pPvJIOOQQj1u+3B8rVvz5/fz58NZb3kN+5pn++r//HcpEt1z339+Hdb75pifwbdv6P67d\nukGHDlCtWnzvhWy96dPhmWd8CK/m14psrn59X70gNy+8AO3a+e/Qf/xj27VLRCQ/FtLppgHM7BSg\nD1APmAs8GEJ4MiWmB77OeTVgPHB1CGF20v4KwANAO6AC8FYU83NSTDVgMF4hPhsYAXQNIaxJiqkD\nPAoch6+n/h/g9hBCdi5tbwxMmTJlioa1i8g28eqrnhxPmODPjzrKh1Oefvrmc8IXL4ZPPoGJE32I\n+mefwbp1f+4vU2bz4Zm77OLHuOAC/z4/kyd7O0aM8N75ww6D446DY4+Fo4/2tYVl+7dxIzz0ENx5\nJ9Sp4z2ClSrF3SqR4iUEOOEEv8k5fbrmpotI0Usa1t4khDA1t7i0k/PiTMm5iBTEzz/Dqaf6mrsP\nPuhDJfOzYQP83/9B377eo92hgx9jt93yfy3A+vUwb56fq2pVT8DMtu46wKsbv/MOfPABfPihn8PM\ne+mPPdbXDq5QwecwV6iw+fd77+0FmCQe33zjy6VNngzXXefD2rfksygifzVjhq+T3qOH/64WESlK\nSs5zoORcRNK1ahUcfzx8/72vtbvHHl6Eq1mz3F+zZIkPmXz/fU/Ou3UrnMS6KHz/vSfpicf330N2\njmOPoGxZTw7vuktJ+rb0xx/Qpw/07u2jLYYN82kPIrJ1brkFBg+GmTNhr73ibo2IlGRbmpynWxBO\nRKTUWL8ezj4bZs2Ct9/2IcTVqnli1Lu3946n+uILaNoUvvzSe6hvuGH7TczBe8M7dYInn/Tq3xs3\nejK4apXfZPjxR9/+zTd+o2HkSKhXz6/rl1/ibn3JN2WKf5569vRlor74Qom5SGG56y6v3N6tW9wt\nERFxSs5FRHKQne29xO+95/PGGzWCv/3N547fcYcPhTzmGE9cE4YP98SpZk1Pqo4/Prbmb5Vy5Xy4\n9C67QO3asO++Xrjuhhtgzhy//qFDffu//uUF6qRwheC95Ucc4fUGPvvMh7FryTSRwrPzztC/P7zy\niheHExGJm5JzEZEc3HabD18fPtznjCfssIOvozt+PCxa5IXVhg3zOcAdO8L55/u+unXja3tRqlLF\nE/K5c6FLF68Wvs8+vqbwkiVxt65k2LABrrjC12y+9VafY96oUf6vE5H0nXuu/46/9lpfHUNEJE5K\nzkVEUgwY4EnnwIFw3nk5xxx5pA9dP+ccuPRSX1f8kUd8eHhpWNpql118mPt33/kNiR49fD7++ef7\naIPc5q1L3latgn/+E556yh/33OM3hESkaJj5smvffw8PPBB3a0SktFNyLiKS5IUXfPj2LbdA1655\nx1ap4sn4f//ry5916bJ9zy8vCrVr+02JH3/0YdhffQWtWvk6w/ff70vE5WTDBq+CP2+eD+EW+Okn\nr5j/8ccwejR07hx3i0RKhwMP9Hnn99zjv5NEROKSVnJuZt3NLDvlMSMlppeZLTSzNWb2jpnVS9lf\nwcyGmNkSM1tpZiPMbLeUmOpm9pyZLTezZWY21Mwqp8TUMbPRZrbazBaZWV8z080GESmwceN8aHrH\njp5obql//MOLdpVmNWv6P7czZsBHH0GLFtC9u1d1P+UUfxxxhBeTq1bNe4MzMrwg3VVXqad9xgx/\nzxYt8mkRJ50Ud4tEShcVhxOR7UG5ArxmOtAKSPQPbapXbGa3AtcAHYHvgbuBsWbWIISwPgobCJwM\ntAVWAEOAkcDRSed4HsiIzlMe+A/wGHBBdJ4ywBhgIdAcqA0MB9YDdxbgmkSkFPv2Wxg0yHvBW7Xy\nYmelrQe8sJjB0Uf7Y+BAn7c/erSv096woQ+HTzxq1oTZs70K+dq1Pne/bNm4r2Db+/BDOOMMv5Ex\nZgzUqRN3i0RKn0RxuPPP99FQJ58cd4tEpDRKa51zM+sOnB5CyHGRcDNbCPQLIQyInlcBFgOdQggv\nRc9/Ac4PIbwSxdQHZgLNQwiTzawB8A2+BtwXUUxrYDSwZwhhkZmdDLwO7B5CWBLFXAH0AXYNIeSw\nwJHWOReRP4Xgy6M99JD/I7brrnDllV6Aq3Ll/F8vhef55320wtlnewG+kjzHOjsbFi70gnpz5/r6\nyv37+82MkSOhatW4WyhSeoXgxeGWLPEpOmU0HlNECsmWrnNekJ7z/c3sR2AdMAm4PYTwg5ntA9QC\n3k0EhhBWmNmnQAvgJaBpdM7kmFlmNj+KmYz3hC9LJOaRcUAAjgBei2KmJRLzyFjgUeAg4KsCXJeI\nlAKrV8Mzz3hPeVaWV1t/6invLdEyVfFo3x4qVIB27bxa8gsv+POS4LvvfCTG1KmejM+bB+vX/7l/\nt918yb6BA6F8+fjaKSI+8ufuu+Goo/xm2TnnxN0iESlt0k3OPwE6A7OA3YEewEdmdjCemAe8pzzZ\n4mgf+FD19SGEFXnE1AJ+Tt4ZQthoZr+mxOR0nsQ+Jecispk//oDBg30ZtBUr4Mwz4fHH/Z8wDWGP\nX9u2vtZw27Y+xHvUqOJb9X7jRl8zecgQ/1qtmhd6O/VUX3Yu8dh7b43SENnetGzpNR969vTfR+o9\nF5FtKa3kPIQwNunpdDObDMwDzgWyCrNhRalbt25UTRk72K5dO9q1axdTi0SkKL37rq9DnpUFl1/u\na5jvtVfcrZJUbdr4/PR//tO/f/112GmnuFu15ZYu9Xnz//6395I3aeLPzzvP59yLSPHQs6cXaHz5\n5dyX0xQRyU1mZiaZmZmbbVu+fPkWvbYgw9o3CSEsN7NvgXrAB3iRuAw279XOABJD1BcB5c2sSkrv\neUa0LxGTWr29LFAjJebwlOZkJO3L04ABAzTnXKQUmDcPbrzRhycedRRMmeLD2GX71aqV9za3aQOt\nW/uSdmvX/vlYs+bPry1awGmnxTvyYcECeP99b/PIkT5n9bzzIDMTmjXTqAyR4qh5c1+Fo2dPr4VR\nGgtVikjB5dTpmzTnPE9blZyb2U54Yv50CGGumS3CK6x/He2vgs8THxK9ZApe3b0VkFwQri4+f53o\nazUza5Q07zxRHf7TpJg7zKxm0rzzk4DlwGZLu4lI6bN2LfTt68uh1agBzz3n85mVKBUPRx/ty9qd\nfLIPcU/YYQfvga5YEcqV85/v3//uRf0OPnjbtO3nn+GDD+C99/zxv//59kMO8X/kL77YiwuKSPHW\ns6cv//jSS/73Q0RkW0grOTezfsAb+FD2PYCewB/AC1HIQOBOM5uNL6XWG1iAF3FLFIgbBvQ3s2XA\nSmAQMCGEMDmKyTKzscATZtYFX0rtYSAzhJDoFX8bT8KHR8u37R6da3AI4Y+03wURKTFmzvQ1tX/8\nEW64Af7v/3yJHClemjWDH36AVas8GU8k5MnGjPE1iQ89FLp08XoCNWqkf64VK3yd8W++8QJuq1Z5\nz/yaNV5AMPH90qUwa5a/5oAD4MQT4d574bjjfFk4ESk5mjXzETw9e8K556r3XES2jXR7zvfE1yDf\nBV8S7WN8CbSlACGEvmZWCV+TvBowHjg5aY1zgG7ARmAEUAF4C7g65TztgcF4lfbsKLZrYmcIIdvM\nTsWrs08EVuNroXdP83pEpITp2dO/Tp8Of/tbvG2RrVOpUt5ztU85BU44AR5+2H/umZnQu7fXFUhN\n5EOARYtgzhzv7f7mmz8f8+d7jJmvMV61qp+3cmX/WqOGr0FepQrcdRccfzzUrl101y0i24cePeDw\nw/13ywUXxN0aESkN0lrnvLjTOuciJdsPP3gV7IED4Zpr4m6NbEuLF8Mdd/iyeAcdBB06+HzwuXM9\nIf/+e1i37s/4ffbxuOTHAQeocJuIbO6f//RiojNm/PWmn4jIlirKdc5FRLZLQ4Z4de/OneNuiWxr\nGRleGb1LFx/qfs89vlTZvvt6Ybl99vHvE8uYaQkzEdkSPXr4ygvPPw8dO8bdGhEp6ZSci0iJsHq1\nr1t+6aXFa/ktKVxNm8L48XG3QkRKisaN4fTTfcpM+/bqPReRolUm7gaIiBSG4cNh+XINZxcRkcLV\nowfMng3PPht3S0SkpFNyLiLFXna2L6d15pk+lFlERKSwHHYYnHWWrwjxh9YEEpEipORcRIq9t9/2\ngj3XXx93S0REpCTq3t0LTD7zTNwtEZGSTMm5iBR7Awd6wZ6WLeNuiYiIlESHHALnnQc33ggTJ8bd\nGhEpqbYqOTez28ws28z6p2zvZWYLzWyNmb1jZvVS9lcwsyFmtsTMVprZCDPbLSWmupk9Z2bLzWyZ\nmQ01s8opMXXMbLSZrTazRWbW18x0w0GkFJkxA8aO9V5zs7hbIyIiJdXjj8Ohh8KJJ8K4cXG3RkRK\nogInsmZ2OHA58FXK9luBa6J9zYDVwFgzK58UNhBoA7QFjgFqAyNTTvE80ABoFcUeAzyWdJ4ywBi8\n4nxzoBPQGehV0GsSkeJn0CCoVQvOPTfuloiISElWpQr8979w7LHQpg289lrcLRKRkqZAybmZ7QQ8\nC1wK/JayuyvQO4TwZghhOtART77PiF5bBbgY6BZC+DCE8AVwEdDSzJpFMQ2A1sAlIYTPQwgTgWuB\n882sVnSe1sABQIcQwrQQwljgLuBqM9NCFyKlwNKlPv/v6quhfPn840VERLZGpUrw6qu+vFrbtvDc\nc3G3SERKkoL2nA8B3gghvJe80cz2AWoB7ya2hRBWAJ8CLaJNTfHe7uSYWcD8pJjmwLIocU8YBwTg\niKSYaSGEJUkxY4GqwEEFvC4RKUaeeMIrtV9xRdwtERGR0qJ8ecjMhI4d4cIL4d//jrtFIlJSpN3D\nbGbnA4fhSXaqWngCvThl++JoH0AGsD5K2nOLqQX8nLwzhLDRzH5NicnpPIl9XyEiJdYff8DgwXDB\nBbDrrnG3RkRESpOyZWHoUB/q3qULrFgBt9wSd6tEpLhLKzk3sz3x+eInhBCK7UqP3bp1o2rVqptt\na9euHe3atYupRSKSrhEj4McfoWvXuFsiIiKlUZkyMGAAVK0Kt94KCxfCTTfBnnvG3TIRiVNmZiaZ\nmZmbbVu+fPkWvTbdnvMmwK7AVLNNdZHLAseY2TX4HHDDe8eTe7UzgMQQ9UVAeTOrktJ7nhHtS8Sk\nVm8vC9RIiTk8pX0ZSftyNWDAABo3bpxXiIhsx0Lwf4hatYKGDeNujYiIlFZm0LMnVKsGd9zhRUqP\nPRY6dPA56dWrx91CEdnWcur0nTp1Kk2aNMn3tenOOR8HNMSHtR8aPT7Hi8MdGkKYgyfGrRIviArA\nHQEkVoWcAmxIiakP1AUmRZsmAdXMrFHSuVvhif+nSTENzaxmUsxJwHJgRprXJSLFyCefwGef+fJp\nIiIicevWDRYvhiefhHLlvBZKrVpw5pk+0mvdurhbKCLFQVrJeQhhdQhhRvIDXyptaQhhZhQ2ELjT\nzE4zs4bAM8AC4LXoGCuAYUB/MzvOzJoATwITQgiTo5gsvLjbE2Z2uJm1BB4GMkMIiV7xt/EkfLiZ\nHWJmrYHewODiPOReRPLXty/svz+cckrcLREREXFVqkDnzvDOO7BgAfTp41/POQdq14ZHH4WNG+Nu\npYhszwq8znmSsNmTEPriifRjeC93ReDkEML6pLBuwJvACOADYCG+5nmy9kAW3lv/JvARsKkmcwgh\nGzgV2Ij3yj8D/AfoXgjXJCLbqa+/9mVsbr/d5/uJiIhsb3bf3XvTP/sMsrLgrLPgqqugeXP4/PO4\nWyci2ysLIeQfVUKYWWNgypQpUzTnXKSYOv98+PRT+PZb2GGHuFsjIiKyZSZN8sruX38NV14J99yj\nOekipUXSnPMmIYSpucWp30lEio2sLHjpJbjtNiXmIiJSvLRo4b3mAwbAs89C/frwzDNe5HRbWrXK\nbxDMnu0V5n/7Ddav3/btEJG/SnudcxGRuNx7L+yxh8/pExERKW7KlfMlQM85B268ETp1gieegIsu\n8hVI9tqraM67Zg2MHg0vvuhfcypQV7YsVK4MO+/sc+Rr1/a/uXvs8ef3u+/uMRUrwo47+tfy5b1q\nvYhsPSXnIlIsfPcdPP+89zhUqBB3a0RERAqudm3IzIRLLoE774RLL/We63r14IQT/HH88VCjxl9f\nGwKsXQsrVnhCvdNOniinJsjr1sF//+sJ+RtveILepAn06gVHHeW95atX+/bEY/VqWL7ce9R//BE+\n/ti/Ll2a+7WYeZK+005+g6FDBzjpJI1wEykIJeciUizcdx/UrOn/wIiIiJQEiUT811/hgw9g3Dh4\n913497896T3sMKhUyRPx5Edq1fdEkp78+PZbWLkSDj3UbwCcey7st1/B2rluHfz0Eyxa5En82rV/\nPtat869LlsArr/hNh5o1/Xzt28ORR6pnXWRLqSCciGz35s3z3oT77oObboq7NSIiIkVr/nxP0j/6\nCLKzfZm2qlX9a+Kx886+b9Uqf6xcufn3e+7pCXL9+tuu3SH4fPbnnvMkfcEC2HtvT9KPOsr/lu+1\nlw+FFylNtrQgXFrJuZldCXQB9o42fQP0CiG8lRTTC7gUqAZMALqEEGYn7a8A9AfOAyrg65lfFUL4\nOSmmOjAYXyotGxgJdA0hrE6KqQP8GzgOWIkvpXZbtMRabu1Xci5SDF11lReC+/577w0QERGR7Vt2\nNowf74n6yy974TnwZVD32st78evV80ejRt7DvuOO8bZZpKhsaXKe7rD2H4Bbgf8BBnQGXjOzw0II\nM83sVuAaoCPwPXA3MNbMGiStcz4QOBlf13wFMARPvo9OOs/zQAbQCiiPr1/+GHABgJmVAcbg66M3\nB2oDw4H1wJ1pXpOIbMd+/BGGDYPu3ZWYi4iIFBdlysCxx/rj0Uf97/l333mV+Nmz/ftJk7xy/apV\nXk+mZUuft96qlc+PL6cJuFLKbPWwdjNbCtwUQnjKzBYC/UIIA6J9VYDFQKcQwkvR81+A80MIr0Qx\n9YGZQPMQwmQza4D3yDcJIXwRxbQGRgN7hhAWmdnJwOvA7iGEJVHMFUAfYNcQwoZc2qqec5Fi5vrr\n4emnfWh7lSpxt0ZEREQKU3Y2fPOND+N/91348EMfll+liif2J50Ep58OderE3VKRgivydc7NrIyZ\nnQ9UAiaa2T5ALeDdREwIYQXwKdAi2tQU761PjpkFzE+KaQ4sSyTmkXFAAI5IipmWSMwjY4GqwEEF\nvSYR2b4sXgyPPebLzigxFxERKXnKlIGGDf1m/BtveGX4iRPh5ps9Se/WDerWhaZN4e67Yfp0rcku\nJVfaybmZHWxmK4HfgUeAM6MEuxaeQC9OecniaB/4UPX1UdKeW0wt4OfknSGEjcCvKTE5nYekGBEp\n5h580Jdi6do17paIiIjItrDDDtCihVeYf/99+OUXX0p1v/3g/vs9kd9/fy8QO26cV5BXsi4lRUFm\ncmQBh+K91GcDz5jZMYXaKpH/Z+++46sosz+Of04ICb1DAOkdKSpNWKSJCEgRRcEgVVQUK/hTWd1d\n27qunVVBsYNABEEFRKUJosCi0qWodJQFRCA0IZA8vz+eG7gEQgkJk8D3/XrN6+bOnDtzJs4Lc+4z\ncx654P3xBwwdCvfeCwULBp2NiIiIBKFAAYiN9cvBg/DVVzBhgm809+KLR2OqVfNL9er+tVIlf9dd\n7tx+OrqoqOOndHPOT023c+fRZfdufwv9xRf7+dtFzqUzLs5Dz3OvDb1dZGYNgPuA5/BN4mI4dlQ7\nBki+RX0LEGVm+VKMnseEtiXHFAs/ppllAwqliKmfIrWYsG0nNWDAAPLnz3/MutjYWGJjY0/1URE5\nR5591v9Pc8CAoDMRERGRzCA6Gtq29cvQoX4u91WrYOVK/7p8OYwf72+HTylbtqOFeo4cPmbnTv/M\n+4lERECVKn6e+Nq1jy6lS2vedjm5uLg44uLijlkXHx9/Wp9Nj4ZwM4ANzrlbTtIQrqdz7qPTbAhX\nDd8Qrl5YQ7ir8d3ZkxvCtQEmcWxDuNuBZ4FizrlDqeSqhnAiWcDIkdCjh3+27NFHg85GREREsgrn\nYPNmWLvWd4Hftw/27z/29c8//ah6wYLHL3nz+ia0S5b4OduTl+Sp4MqWhY4doUMH37BOc7bL6ciQ\nqdTM7F/AF/gGbnmBm4FmwNWhkMHA38xsNX4qtaeAX4EJ4BvEmdk7wEtmthM/P/krwBzn3HehmFVm\nNjUAV7YAACAASURBVAV4y8zuxE+l9ioQ55xLHhWfCqwAPghN31YidKzXUivMRSRrmDYN+vTxyyOP\nBJ2NiIiIZCVmcNFFfkmrmBho0ODoe+dg0yZYvNj/nTJhArz6qi/w27Txhfo110ChQmefv1zYzvS2\n9mLAcHwxHA8sBa52zn0F4Jx7zsxy4eckLwB8A7QNm+McYACQCIwDooEvgbtSHKcb8Bq+S3tSKPZI\nSyjnXJKZtQdeB+YC+/BzoT92hucjIpnIokVw/fXQqpXv0q7bxkRERCRoZr5jfJkyftT8lVf8aPrE\nib7DfI8e/rb55s2hZ0//t0yePEFnLVnRWd/WnpXotnaRzGvdOvjLX6BUKd+dVf9TExERkaxg82Zf\npH/4Icya5Z9tv+EGX6g3b+6fX5cLW4bPcy4ikl62b/e3heXODZMnqzAXERGRrKNkSejXzw8urFsH\nDz8Mc+ZAy5ZQrpzvn7NihaZ8k1NTcS4igdq/3z+rtXMnTJkCxYqd+jMiIiIimVG5cvD3v/tO8nPm\n+M7yQ4ZAjRp+erd77/V/7xw4EHSmkhmpOBeRwBw+7OctXbYMPv8cKlYMOiMRERGRs2fmH9cbNgy2\nbIHPPoPWrX0zuTZtoHBh6NQJ3noLNm7UqLp4ZzzPuYhIenngAX8b+6RJUK9e0NmIiIiIpL8cOaBd\nO784Bz/+6P/+mTwZ7rjDz7VetChcdtmxS6VKel79QqPiXEQCsXWrv83r6af9LV8iIiIi5zszqFXL\nL4MGwY4dMHu2n7Fm0SIYNQqefdbH5skDl14KDRv6UfhGjaB48WDzl4yl4lxEAjFqlJ925Lbbgs5E\nREREJBiFCvnb2zt1Orru99/9nOqLF8MPP8CYMfDCC35bhQq+UE9eatb0f0/J+eGMbpQws7+a2Xdm\nttvMtprZJ2ZW5QRxT5rZZjPbb2bTzKxSiu3RZjbEzLab2R4zG2dmxVLEFDSzUWYWb2Y7zextM8ud\nIqa0mU02s31mtsXMnjMz3fwhksk5B++9B9de6/+nJCIiIiJe0aLQqhU8+KAvzDdu9MuYMb6J7k8/\n+cZyl17qYzt1gsGDYckSf4u8ZF1nOnLeBHgV+CH02WeAqWZW3Tn3J4CZPQzcDfQE1gP/BKaEYhJC\n+xkMtAU6A7uBIcD40P6TjQZigJZAFPA+MAzoHjpOBPA5sBloCJQEPgASgL+d4XmJyDm0YIF/3uq5\n54LORERERCTzK13aL126+Pf798P33/t51WfN8rfIHzzoBz2aNfPzqzdo4G+fz537JDuWTMXcWbQG\nNLMiwDagqXPu29C6zcDzzrmXQ+/zAVuBXs65saH3vwM3Oec+CcVUBVYCDZ1z35lZdWA5fpL2RaGY\n1sBkoJRzbouZtQUmAiWcc9tDMf2AfwNFnXOHT5BvHWDBggULqFOnTprPW0TOzl13waef+m+BdSuW\niIiIyNk5cADmz/dzrc+aBfPmQUKCf8a9UiW45JJjl9Kl/TY5NxYuXEjdunXB17cLU4s722fOCwAO\n2AFgZuWB4sCM5ADn3G4zmw80AsYC9ULHDY/5ycw2hmK+w4+E70wuzEOmh451OTAhFLMsuTAPmQK8\nDtQAlpzluYlIBjhwAEaPhn79VJiLiIiIpIccOfyIebNm/v2BA7Bihb/VPXl56SXYudNvL1AAatc+\ntmCvUQNy5gzuHOQsinMzM/zt6d8651aEVhfHF9BbU4RvDW0Df6t6gnNu90liiuNH5I9wziWa2Y4U\nMSc6TvI2FecimdDEibBrF/TpE3QmIiIiIuenHDmgTh2/JHMOfv312IJ9yhR47TW/LSICqlb1I+0F\nC/oC/lRLvnwabElPZzNyPhS4GGicTrmIyAXgvff8VCBVqwadiYiIiMiFw+zos+vt2x9dv3+/7wWU\nXLCvXw9r1/rBlORl797U95svny/UixWDFi2gTRu44gqIisrwUzrvpKk4N7PXgGuAJs65/4Vt2gIY\nfnQ8fFQ7BlgUFhNlZvlSjJ7HhLYlx6Ts3p4NKJQipn6K1GLCtqVqwIAB5M+f/5h1sbGxxMbGnuxj\nInKWfvsNpk6FN94IOhMRERERAciVyzePa9Ag9ZjDhyE+3i/hRXv4sn49fPABPP+8b0LXsqUv1Nu2\nhXLlztXZBC8uLo64uLhj1sXHx5/WZ8+4IVyoML8WaOacW3uC7ak1hOvpnPvoNBvCVcM3hKsX1hDu\nanx39uSGcG2ASRzbEO524FmgmHPu0AlyU0M4kQA98ww89RRs2eK/ZRURERGR80dSEixdCl98AV9+\nCXPmQGKiv2Oyc2fo2tV3kL/QmtGdbkO4M53nfChwM9AN2GdmMaElR1jYYOBvZtbBzGoBI4Bf8U3c\nCI2WvwO8ZGbNzawu8C4wxzn3XShmFb6521tmVt/MGuOncItzziWPik8FVgAfmFntUDf3p4DXTlSY\ni0iwkuc279xZhbmIiIjI+Sgiws+//te/wtdfwx9/wPjx8Je/wNChRxvPPfEErFoVdLaZzxmNnJtZ\nEr7hW0p9nHMjwuIeB27Hd3P/BrjLObc6bHs08AIQC0QDX4ZitoXFFABeAzoAScA44D7n3P6wmNL4\n7uzNgX34udD/6pxLSiV/jZyLBGTOHP/80YwZcOWVQWcjIiIiIudSQoJ/vHHMGJgwAfbs8R3ju3Tx\nI+v58vklb96jr3nznh8N50535Pys5jnPalSciwTn1lth+nTfYCTijO7ZEREREZHzyYED/tb3MWNg\n0iTflC41pUr50fjwad8qVsxaRfu5mudcROSU9u2DsWNhwAAV5iIiIiIXuhw54Lrr/HL4sB9F3737\n+Nfdu+GXX3wX+Xffhf+FWpHnyuWfXa9VC6pX98vFF/tO9Fn5b00V5yKS4T7+2P8j27t30JmIiIiI\nSGYSGennVS9Y8NSx27YdO0/7ggUwahT8+affnisXVKvmC/Vq1fyc7ZUr+yVv3ow9j/Sg4lxEMtx7\n70Hz5lC+fNCZiIiIiEhWVawYtGrll2RJSbBxI6xYAStXHl2++MI3pEsWE3O0WK9SBWrW9EvZspln\ntF3FuYhkqHXrYOZMGD486ExERERE5HwTEeHnUS9XDq655thtO3f62+J/+QVWr/avK1bAJ5/4OdsB\n8uTxHeRr1fLFeu3a0LAh5Mx5rs9ExbmIZLDhw/1tRJ07B52JiIiIiFxIChaEBg38Es45+O03WLYM\nfvzRLwsWwAcfwMGDEB3tZxlq1Qquugouu+zcjK6rOBeRDOEcfPUVvPWWnyIjd+6gMxIRERERATPf\nBb5UKWjb9uj6w4f9LfEzZvhZhp56CgYNgkKFoGVLX6i3aQNlymRMXmdc/5tZEzObaGa/mVmSmXU8\nQcyTZrbZzPab2TQzq5Rie7SZDTGz7Wa2x8zGmVmxFDEFzWyUmcWb2U4ze9vMcqeIKW1mk81sn5lt\nMbPnzCyTPDEgcmFKTIRx4/w3lFddBUWL+n/UREREREQys8hIf3v7/ffDZ5/Bjh0wezbcdRds2gR3\n3umfUa9VCx5+GL7+Gg4dSr/jp6WQzQ0sBvoDx02SbmYPA3cDtwMNgH3AFDOLCgsbDLQDOgNNgZLA\n+BS7Gg1UB1qGYpsCw8KOEwF8jh/9bwj0AnoDT6bhnETkLB086EfJq1eHG2/0t7J/+SUsWuSbb4iI\niIiIZCVRUdCkCTz5JMyb5xvMjR0L9erB++/7hsdFi/q7RN9/H5Yv93O4p5U5d1x9ffofNksCOjnn\nJoat2ww875x7OfQ+H7AV6OWcGxt6/ztwk3Puk1BMVWAl0NA5952ZVQeW4ydpXxSKaQ1MBko557aY\nWVtgIlDCObc9FNMP+DdQ1Dl3+AT51gEWLFiwgDp16qT5vEXkqF274M03YfBg2LLFz1f58MPHP9sj\nIiIiInK+SEqChQt9V/jPP4f58/1jnWZ+dD25K3yVKhARsZB77qkLvr5dmNo+0/WZczMrDxQHZiSv\nc87tNrP5QCNgLFAvdNzwmJ/MbGMo5jv8SPjO5MI8ZDp+pP5yYEIoZllyYR4yBXgdqAEsSc9zE5Fj\nrV0L//kPvPuuHzXv2RMefBCqVg06MxERERGRjBUR4UfQ69WDv//dj6qvWOE7wv/8s1++/hreftv/\nrXw60rshXHF8Ab01xfqtoW0AMUCCc273SWKKA9vCNzrnEs1sR4qYEx0neZuKc5F05hzMnQsvvQSf\nfuo7YN5/P/TvDyVKBJ2diIiIiEgwChf2t8A3aXLs+qQkP7revv2p93FBdms/fNwN7yeXkODnwStS\nxN+mIBcW5/x0YM5B794XzjXgHOzd629bj4+HpUv9SPl33/nR8aFDoUcPyJUr6ExFRERERDKniIjT\nH8RK7+J8C2D40fHwUe0YYFFYTJSZ5Usxeh4T2pYck7J7ezagUIqY+imOHxO2LVWNGg0gf/78FCzo\nR/4KFIBbbomlW7dYdu+GJUt8E6vFi/3r8uW+C1+BAlCt2vFLqVL+WduNG/2yadPRn7duhYsuOvq8\nQfJSsuTxc+UlJMDOnb4Y2rXL/0csXfr0isHERJ/rtGk+l2rVoEYNuPhi/6VCRklK8kVbwYJ+SoHz\nrXDduBFuvdX/XgE++gjeeSdrjhI7B5s3w7p18Pvvftm27ejPv//ub8dJvv7i4/1/33BXXuk7V7Zt\ne27mehQRERERyUri4uKIi4s7Zl18fPxpffZcNoTr6Zz76DQbwlXDN4SrF9YQ7mp8d/bkhnBtgEkc\n2xDuduBZoJhz7rim9skN4QYOXMBvv9Vh/nxYv95vK1YM8uTxz9GC78xXq5afcP7SSyEmxj8/sGrV\n0WV3yhvzQ4oW9YVqmTJ+v7/+6p85WLvWF9HgRxsrVvTvd+3yRfmffx6/r+LF4fLLjy716/su2OCL\nrGnT/PLVV77Vf+7c/suCNWuO3iFQtOjRQr1iRcieHbJl88VV+Gt0NNSs6bttR57ka5vERPj2Wxg/\nHj7+GH77za8vVQoaN/bLFVdA7dp+v6fLOb/vQ4eOTkmQK9fJc8kozsF778GAAf73/c47PrdbbvG/\n12HDoHPnc5/X6YqPhx9/hGXL/JL8886dR2MiIvztN8WK+WukaFH/RU6BAn7Jn//Y1+LFoVy5wE5J\nRERERCRLWrhwIXXrnroh3BkX56G5xivhR8gXAgOBmcAO59wmM3sIeBg/rdl64Cl8g7YazrmE0D6G\nAm2BPsAe4BUgyTnXJOw4n+NHz+8EooB3ge+ccz1C2yPwo/GbQ8crAYwA3nTO/T2V3I/r1r51q79N\nd/58XxxfeqlfqlXzRWxqnPMj1KtW+eK0RAlfjJcqBTlznvgzhw75gjq5ScDq1f4YBQocHcFPHs3P\nlw82bPB5zZ/vc9yzx49MX3yxb9G/Zo0vfpPnk27VyhfwUVF+FH71aj/qv2LF0dcNG3xxmZTki82k\nJH8u4XLkgEsugTp1/FK3rh/tTy7IP/3Uj7KWKgXXXw+dOvnc5szxMT/84I+fJw80auS/2Ni3zy97\n9x77+uefR4vx1OYIjI72+8qd27/myeMLxgoV/BRdFSsefQ2/xXrfPv87+OWXo7/z9ev9f9s2bfwo\ncPIXHeF++w1uu80/G9Knj3++ukABv237drj9dvjkE98A7ZVXfC6pXSOrVvnj5s8PhQodXXLmPHqX\ngXP+i5UNG47ecbFxo88jMfHolyfhS0SEvwb27vW/+717j/68Z4/fH/jYKlX8F03JS6VK/r9JwYJn\n9uWJiIiIiIicuYwszpvhi/GUHxzunLslFPM4fp7zAsA3wF3OudVh+4gGXgBigWjgy1DMtrCYAsBr\nQAcgCRgH3Oec2x8WUxrfnb05fj7194G/OudS3Ix7JD7LTqWWlOQLveRiPTLSF+QtWqReHJ4u5/z+\n9+71t6gvXAgLFvjXlSuPvbW5YkU/Ynz99X4U/0S3Nh844Av0OXP8smvX0eI6ucBO/jlnTv8FRVSU\nfw1fAPbvP1p4hhf2O3b4OxFWr/bvk5Us6b80+O23oyP64IvrKlX8tAaLF/tiPTLSj/K3aQOtW/sv\nJEaOhHvv9UX+m2+euHGDczBiBNxzjy9wR4yAZs38lx6LFsE33/jl2299MX8iUVG+SM+dG/73P3+e\nyaKj/Rc9F13kfw+JiUeX5C9VEhP9lyh58x79wiL851KlfCFerZqPExERERGRYGRYcZ6VZeXiPCj7\n9vmCfcUKP01A7dqZ67ly5/xz06tX+zsJVq/2z/yXLOmL8eT5BQsXPjbvNWtgyhT48kv/SMC+ff5u\nhd27oXt33/isUKGTH3v9eujVyxfiDRv639O+fb4Yvvzyo90aa9U6+oXCzp3+NXnZu/foXRfJS9Gi\nep5bREREROR8oeL8BFScy4kkJPgR/q++8o8IdOhw+p9NTPS3ts+e7Qv0Jk38YwDR0RmXr4iIiIiI\nZB2nW5xfkFOpiYSLivKPB7RoceafzZbNN40bMCD98xIRERERkQuHbp4VERERERERCZiKcxERERER\nEZGAqTgXERERERERCZiKcxEREREREZGAZfni3MzuMrN1Zvanmf3XzOoHnZOcn+Li4oJOQbIoXTuS\nVrp2JK107Uha6dqRtNK1c/aydHFuZl2BF4HHgMuAJcAUMysSaGJyXtI/OJJWunYkrXTtSFrp2pG0\n0rUjaaVr5+xl6eIcGAAMc86NcM6tAu4A9gO3BJuWiIiIiIiIyOnLssW5mWUH6gIzktc55xwwHWgU\nVF5y/vrtt9+CTkGyKF07kla6diStdO1IWunakbTStXP2smxxDhQBsgFbU6zfChQ/9+nI+U7/4Eha\n6dqRtNK1I2mla0fSSteOpJWunbMXGXQC51gOgJUrVwadh2RBhw4dYuHChUGnIVmQrh1JK107kla6\ndiStdO1IWunaSV1Y/ZnjZHHm7wTPekK3te8HOjvnJoatfx/I75y77gSf6QaMOmdJioiIiIiIiHg3\nO+dGp7Yxy46cO+cOmdkCoCUwEcDMLPT+lVQ+NgW4GVgPHDgHaYqIiIiIiMiFLQdQDl+PpirLjpwD\nmFkX4H18l/bv8N3bbwCqOed+DzA1ERERERERkdOWZUfOAZxzY0Nzmj8JxACLgdYqzEVERERERCQr\nydIj5yIiIiIiIiLng6w8lZqIiIiIiIjIeUHFuYiIiIiIiEjAVJyLiIiIiIiIBEzFuYiIiIiIiEjA\nVJyLiIiIiIiIBEzFuYiIiIiIiEjAVJyLiIiIiIiIBEzFuYiIiIiIiEjAVJyLiIiIiIiIBEzFuYiI\niIiIiEjAVJyLiIiIiIiIBEzFuYiIiIiIiEjAVJyLiIiIiIiIBEzFuYiIiIiIiEjAVJyLiIiIiIiI\nBEzFuYiIiIiIiEjAVJyLiIiIiIiIBEzFuYiIiIiIiEjAVJyLiIiIiIiIBEzFuYiIiIiIiEjAzrg4\nN7MmZjbRzH4zsyQz63iCmCfNbLOZ7TezaWZWKcX2aDMbYmbbzWyPmY0zs2IpYgqa2SgzizeznWb2\ntpnlThFT2swmm9k+M9tiZs+Zmb5wEBERERERkSwlLYVsbmAx0B9wKTea2cPA3cDtQANgHzDFzKLC\nwgYD7YDOQFOgJDA+xa5GA9WBlqHYpsCwsONEAJ8DkUBDoBfQG3gyDeckIiIiIiIiEhhz7rj6+vQ/\nbJYEdHLOTQxbtxl43jn3cuh9PmAr0Ms5Nzb0/nfgJufcJ6GYqsBKoKFz7jszqw4sB+o65xaFYloD\nk4FSzrktZtYWmAiUcM5tD8X0A/4NFHXOHU7ziYmIiIiIiIicQ+l6C7iZlQeKAzOS1znndgPzgUah\nVfXwo93hMT8BG8NiGgI7kwvzkOn4kfrLw2KWJRfmIVOA/ECNdDolERERERERkQwXmc77K44voLem\nWL81tA0gBkgIFe2pxRQHtoVvdM4lmtmOFDEnOk7ytiUpkzOzwkBrYD1w4NSnIyIiIiIiInJWcgDl\ngCnOuT9SC0rv4jyzaw2MCjoJERERERERueDcjO+tdkLpXZxvAQw/Oh4+qh0DLAqLiTKzfClGz2NC\n25JjUnZvzwYUShFTP8XxY8K2nch6gJEjR1K9evXTOB2Ro1q1asW0adOCTkMCkOSSuP/L+2lfuT1X\nV7r6jD+va0fSSteOpJWuHUkrXTuSVrp2Urdy5Uq6d+8OoXo0NelanDvn1pnZFnyH9aVwpCHc5cCQ\nUNgC4HAoJrwhXBlgXihmHlDAzC4Le+68Jb7wnx8W84iZFQl77vxqIB5YkUqKBwCqV69OnTp1zvJs\n5UKTPXt2XTcXqEk/TWLO4Tls+G0D919/Pzkic5zR53XtSFrp2pG00rUjaaVrR9JK185pOemj1WmZ\n5zy3mV1iZpeGVlUIvS8dej8Y+JuZdTCzWsAI4FdgAhxpEPcO8JKZNTezusC7wBzn3HehmFX45m5v\nmVl9M2sMvArEOeeSR8Wn4ovwD8ysdqib+1PAa865Q2d6XiKnctFFFwWdggTkhXkvULVwVTbv2cyb\nC94848+ndu1sit/EzHUzzzY9OY/p3x1JK107kla6diStdO2cvbR0a6+Hv0V9Ab7524vAQuAJAOfc\nc/hCehh+lDsn0NY5lxC2jwHAZ8A4YBawGT/nebhuwCp8l/bPgNlAv+SNzrkkoD2QCMzFfwnwPvBY\nGs5J5JT0D86F6fvfvmf2htk80/IZel3Si3998y/2Jew7o32kvHacc4xcOpKar9ek5YiWzFg7I5VP\nyoVO/+5IWunakbTStSNppWvn7J3xbe3Oua85RVHvnHscePwk2w8C94SW1GJ2Ad1PcZxN+AJdRCRD\nvDjvRSoWrEjHqh25rMRljFw6kiHfD+Ghxg+laX9/7P+DOyffyUcrPuLmWjezec9menzSg6V3LqVI\nriLpnL2IiIiIZBXpOs+5yPksNjY26BTkHFu/az0frfiIgY0Gki0iG+UKlKPvZX15ds6z7D6YcjbI\n1CVfO1NWT6HW67WYvnY6H3b+kJHXj2Tk9SNJSEyg78S+OOcy6lQki9K/O5JWunYkrXTtSFrp2jl7\ndiH9MWhmdYAFCxYsULMCETml+7+8nw+WfsCmAZvIlT0XAL/u/pVKr1TikSaP8I9m/zit/ew/tJ+H\npj3EkO+H0KpCK9679j0uynf01q9JP02i44cdGXLNEPrX758h5yIiIiIiwVi4cCF169YFqOucW5ha\n3IU2z7mIyGnZ+edO3l74NgMaDjhSmAOUyleKO+vdyYvzXuTuBndTKGehk+5n0f8WETs+lg3xG3i1\n7av0r9+fCDv2pqUOVTtwV/27eGDqAzQt25SaxWpmyDmJiIhI1rJx40a2b99+6kAJXJEiRShTpsxZ\n7UPFuYjICby54E0OJR3i7gZ3H7dt0BWDeHPhm7w490Webvl0qvuYumYq14+5niqFq7Co3yKqFamW\nauzzrZ7n6w1fEzs+lu9u/Y6c2XOmy3mIiIhI1rRx40aqV6/O/v37g05FTkOuXLlYuXLlWRXoKs5F\nRFJISEzgle9eoUftHsTkiTlue0yeGO5tcC//mf8f7mt4H8VyFzsuZuTSkfSZ0IfWFVsz5oYx5I7K\nfdJj5syek7jOcdR7sx4PTnuQ1655LdVY5xxrd66lbIGyREbon3EREZHz0fbt29m/fz8jR46kevXq\nQacjJ7Fy5Uq6d+/O9u3bVZyLiKSnD3/8kM17NjOw0cBUYx5s/CBDfxjKs98+y4utXzxm2wtzX+DB\naQ9yy6W3MKzDsNMuoGsWq8mLV7/I3V/cTeuKrelQtcMx23/d/Ssjlozg/cXv88uOXyicszAdq3bk\numrX0apiK3JE5jjzkxUREZFMrXr16uqXdYFQt3YRkTDOOV6Y+wLtKrfj4qIXpxpXKGchBjQcwNAf\nhrJ5z2YAklwSA6cM5MFpD/Jok0d5u+PbZzyy3b9+fzpU6UCfCX3YvGczBw4fYMyPY2gzsg1lB5fl\nn7P/ScNSDRnfZTy31bmNuZvm0vHDjhR5rghdPupC3LI44g/En9XvQERERETOPRXnIiJhpq+dzrJt\ny3ig0QOnjB3QcAA5I3Pyr2/+RUJiAt0/7s7g/w7mtbav8c8r/4mZnfHxzYx3r32XqGxRtBjegpIv\nluSm8TexJ2EPw9oPY8v/bWHEdSO4vvr1PHPVM6y6exUr+q/gkSaPsG7XOrp93I1iLxRj2A/D0nL6\nIiIiIhIQ3dYuIhLmhXkvUKdEHZqXa37K2Pw58vNQ44f4x8x/sHTrUub/Np+xN47lhotvOKsciuQq\nwujOo7n3i3u5o94d9L60N1UKV0k1vnrR6lQvWp1HmjzCxviNPD37ae6YfAe5sueixyU9zioXERER\nETk3VJyLiIQs3bqUqWumMur6Uac96n1Pg3t4+b8vs2TrEqZ2n0qzcs3SJZfm5Zqz9M6lZ/y5MvnL\n8Eb7N0h0ifSZ0Ic8UXm4rvp16ZKTiIiIiGQcFeciIiEvzXuJ0vlKc+PFN572Z3JH5earnl+RM3tO\nKhSskIHZnT4zY1j7YexN2EvXcV2ZFDuJ1pVaB52WiIiIiJyEnjkXEQHilsUxatko7rv8PrJny35G\nn61RrEamKcyTZYvIxgfXfUDrSq25bsx1fLPhm6BTEhERETli+PDhREREsHDhwhNub968ObVr1z7H\nWQVLxbmIXNASkxJ5aNpDdPu4G7E1Y7m7wd1Bp5RusmfLzkc3fkSj0o1oN7odP2z+IeiURERERI44\n2WOEaWmsm9WpOBeRC9aOP3dwzehreGneS7zc+mWGdxpOdGR00GmlqxyROZhw0wRqFKtB65Gt+XHb\nj0GnJCIiInJOHThw4ITrnXMcPHjwHGeTOhXnInJB+nHbjzR4qwE/bP6BKd2ncH/D+8/bb2jzROXh\n826fUzpfaVp90Ipf/vgl6JREREREzsh7771Hy5YtiYmJIUeOHNSoUYM33njjuLhy5crRsWNHpk6d\nSv369cmZMydvvvkmABEREdx7772MHj2amjVrkiNHDr744gvKly/Pddcd30D34MGD5M+fnzvv+MZt\nQQAAIABJREFUvDPDzw/UEE5ELkAfr/yYnp/0pGKhikzrMY3yBcsHnVKGK5izIFN7TKXZ+81o/G5j\nJtw0gUalGwWdloiIiFzg4uPj+eOPP45Z55zj0KFDx6x74403qFmzJtdeey2RkZFMmjSJ/v3745w7\npng2M1atWkW3bt3o168ft99+O1WrVj2yfcaMGYwdO5a7776bIkWKUKFCBbp3787zzz/Prl27KFCg\nwJHYiRMnsnfvXnr0ODdT06o4F5HzSmJSIku2LuHg4YMkukQSkxJJcklHfp61fhb/nvNvutTowrsd\n3yV3VO6gUz5niuUuxrd9vqXTmE60GN6CD677gBtrnH5nehEREcm89h/az6rtqzL0GNWKVCNX9lzp\ntj/nHC1btkx1e82aNY/8PHv2bKKjjz5+2L9/f9q2bctLL7103Mj2mjVrmDJlClddddVx+/z555/5\n8ccfjynYc+bMydNPP83YsWO5/fbbj6wfOXIk5cqV4y9/+Uuazu9MqTgXkfPK+4vf59ZJt6a6PcIi\neKblMzzc+OHz9jb2kymcqzDTe0znlom30GVcF/6989881PihC/J3ISIicj5ZtX0Vdd+sm6HHWHD7\nAuqUqJNu+zMzhg4dSuXKlY/bNnDgQJKSko68Dy/Md+/ezaFDh2jatClTp05lz5495M2b98j28uXL\nn7AwB98FPrwwB6hcuTKXX345o0aNOlKc79y5ky+//JJBgwad1TmeCRXnInJembZ2GnVK1GFEpxFk\ni8hGhEWQzbId+TlPVB4K5SwUdJqBio6MZuR1I6lYsCKDZgxizc41DLlmyBlPISciIiKZR7Ui1Vhw\n+4IMP0Z6q1+/PnXqHF/wFyxY8Jjb3efMmcNjjz3Gf//7X/bv339kvZkRHx9/XHGemnLlyp1wfc+e\nPbnnnnvYtGkTpUuXZuzYsRw+fJju3bun4azSJt2LczOLAJ4AbgaKA5uB951z/0wR9yRwK1AAmAPc\n6ZxbHbY9GngJ6ApEA1OA/s65bWExBYHXgPZAEjAeuM85ty+9z0tEMj/nHLPWz6LPpX2oUaxG0Olk\nambGky2epELBCtw26TY2xG9g7A1jyZ8jf9CpiYiISBrkyp4rXUe1M5M1a9Zw1VVXUb16dV5++WVK\nly5NVFQUkydPZvDgwceMsIO/TT01qW276aabGDBgAKNGjWLQoEGMGjWKevXqnXBUP6NkRLf2QUA/\noD9QDXgIeMjMjkwebGYPA3cDtwMNgH3AFDOLCtvPYKAd0BloCpTEF9/hRgPVgZah2KbAsPQ/JRHJ\nCn764ye27ttK83LNg04ly+h9aW+mdJ/C/F/nc8V7V7Bu57qgUxIRERE5xqRJk0hISGDSpEncdttt\ntGnThiuvvJIcOXKk2zEKFixIu3btGDVqFBs3bmTOnDn07Nkz3fZ/OjKiOG8ETHDOfemc2+ic+xiY\nii/Ck90HPOWc+8w59yPQE198dwIws3zALcAA59zXzrlFQB+gsZk1CMVUB1oDfZ1zPzjn5gL3ADeZ\nWfEMOC8RyeRmrZ9FNstG4zKNg04lS7my/JXM6zuPvQl7qfJaFbp81IWZ62binAs6NREREREiI/0N\n3+Ej5PHx8bz//vvpepwePXqwfPlyHnzwQSIjI+natWu67v9UMqI4nwu0NLPKAGZ2CdAY+Dz0vjz+\ndvcZyR9wzu0G5uMLe4B6+Fvuw2N+AjaGxTQEdoYK92TTAQdcnu5nJSKZ3tcbvqb+RfXJE5Un6FSy\nnOpFq7O432JevPpFlm1bxpUjruTioRfzn//+h10HdgWdnoiIiJyHTncg4OqrryZ79uy0b9+eoUOH\n8uyzz1KvXj1iYmLSNZ927dpRuHBhPvroI1q1akWRIkXSdf+nkhHF+b+BMcAqM0sAFgCDnXMfhrYX\nxxfQW1N8bmtoG0AMkBAq2lOLKQ5sC9/onEsEdoTFiMgFIvl58+ZlmwedSpaVP0d+7r38Xlb0X8HM\nXjOpHVOb/5v2f5R8sSR9J/Rl6dalQacoIiIi55FTzRaTvL1KlSqMHz+eiIgIHnzwQd58803uuOMO\n7r333hN+JrX9nmwbQPbs2enatStmds5vaYeM6dbeFegG3ASsAC4F/mNmm51zH2TA8URE+PmPn9my\ndwvNyjULOpUsz8xoXq45zcs1Z8veLbyz8B2GLRjGiKUj+HfLfzOw0UBNvSYiIiJnpVevXvTq1SvV\n7TNnzjzmfbt27WjXrt1xcb179z7m/dq1a1PdZ2Ji4inzioqKIm/evHTs2PGUsektI4rz54BnnHMf\nhd4vN7NywF+BD4AtgOFHx8NHz2OA5FvUtwBRZpYvxeh5TGhbckyx8AObWTagUFjMCQ0YMID8+Y/t\nSBwbG0tsbOxpnJ6IZEZHnjcvrefN01PxPMV5tOmjPNT4IR796lH+b9r/8c3Gb3jv2vcomLNg0OmJ\niIiIpJuDBw8ycuRIbrjhhjQ3m4uLiyMuLu6YdfHx8af12YwoznMBKb+SSCJ0C71zbp2ZbcF3WF8K\nRxrAXQ4MCcUvAA6HYj4JxVQFygDzQjHzgAJmdlnYc+ct8YX//JMl+PLLL59wLj0RybpmbZhFvZL1\nyBud99TBcsayZ8vOc62eo0mZJvT6tBd13qzDRzd+RL2S9YJOTUREROSs/P7770ybNo1x48axY8eO\nE94uf7pONOi7cOFC6tate8rPZsQz55OAv5nZNWZW1syuAwYAH4fFDA7FdDCzWsAI4FdgAhxpEPcO\n8JKZNTezusC7wBzn3HehmFX4uc/fMrP6ZtYYeBWIc86ddORcRM4vR5431xRqGa5D1Q4s7LeQormK\n0vjdxgz5boi6uouIiEiWtmLFCrp37868efN49dVXqV27diB5ZMTI+d3AU/hR8GLAZuD10DoAnHPP\nmVku/JzkBYBvgLbOuYSw/QzAj8CPA6KBL4G7UhyrG/Aavkt7Uij2vvQ/JRHJzJKfN1dxfm6UK1CO\nb2/5lgenPsjdX9zN7I2zeavDW+SLzhd0aiIiIiJnrFmzZsdM0xaUdC/OnXP7gIGh5WRxjwOPn2T7\nQfy85fecJGYX0D0teYrI+UPPm597Udmi+E/b/9CkbBP6TuxL/bfqM73HdErnLx10aiIiIiJZUkbc\n1i4ick59veFrPW8ekBsuvoEfbvuBhMQEWgxvwab4TUGnJCIiIpIlqTgXkSxNz5sHr3LhyszsNZNE\nl0jz4c1VoIuIiIikgYpzEcnSftnxC//b+z+aldX85kEqV6Acs3rNIskl0Xx4czbGbww6JREREZEs\nJSMawomInDNHnjcvo+fNg1a2QFlm9ZpFi+EtaDG8BTN7zaRM/jJBpyUiIpKlrVy5MugU5BTS67+R\ninMRydJmrZ9F3ZJ11Sk8kyhboCwze82kxfAWNH+/ObN6z1KBLiIikgZFihQhV65cdO+u/tdZQa5c\nuShSpMhZ7UPFuYhkWcnPm/eo3SPoVCRM2QJlmdV7Fs3fb64CXUREJI3KlCnDypUr2b59e9CpyGko\nUqQIZcqc3d87Ks5FJMtKft5czeAynzL5yxxToE+KnUSNYjWCTktERCRLKVOmzFkXfJJ1qCGciGRZ\net48c0su0HNH5abB2w0Yvnh40CmJiIiIZFoqzkUky/p6w9d63jyTK5O/DPNvnU/XGl3pPaE3fSf0\nZf+h/UGnJSIiIpLpqDgXkSzpyPzmZZsHnYqcQq7suXj32nd5t+O7xP0YR8O3G/LT9p+CTktEREQk\nU1FxLiJZ0uodq9m8ZzPNyml+86yiz2V9mH/rfBISE6j3Vj3G/Dgm6JREREREMg0V5yKSJc1aP4sI\ni+CKMlcEnYqcgVoxtfj+tu/pWLUjN42/if6T+5OQmBB0WiIiIiKBU7d2EcmSZm2YRd0Set48K8ob\nnZeR142kaZmm3PPFPRTLXYzHmz8edFoiIiIigdLIuYhkOUeeN9cUalmWmdGvXj8eaPQAz855lvW7\n1gedkoiIiEigVJyLSJaT/Ly5ivOs79Gmj1I4Z2EGThkYdCoiIiIigVJxLiJZjp43P3/kicrDC1e/\nwCerPmHqmqlBpyMiIiISGBXnIpLlfLj8QxqXbqznzc8TXWt0pVnZZtz7xb1qDnce2pewj5W/rww6\nDRERkUxPxbmIZCkrf1/JV+u+4o56dwSdiqQTM+PVtq+yesdqXpn/StDpSDrafXA3V464ktpv1Gbm\nuplBpyMiIpKpqTgXkSzl9R9ep1juYnSu3jnoVCQd1YqpRf/6/Xni6yfYvGdz0OlIOtiXsI/2o9vz\n0/afqF+yPp3HduaXP34JOi0REZFMK0OKczMraWYfmNl2M9tvZkvMrE6KmCfNbHNo+zQzq5Rie7SZ\nDQntY4+ZjTOzYiliCprZKDOLN7OdZva2meXOiHMSkeDtTdjL8CXDufWyW4mOjA46HUlnT7Z4kpyR\nOXl4+sNBpyJn6cDhA3Qa04mF/1vIFzd/weRukymWuxgd4jqw68CuoNMTERHJlNK9ODezAsAc4CDQ\nGqgOPADsDIt5GLgbuB1oAOwDpphZVNiuBgPtgM5AU6AkMD7F4UaH9t8yFNsUGJbe5yQimcOopaPY\nm7CXfvX6BZ2KZIACOQrwTMtnGLl0JN9u/DbodCSNEhITuGHsDXy78Vs+6/YZjUo3omDOgkyKncS2\nfdvo8lEXDicdDjpNERGRTCcjRs4HARudc7c65xY45zY456Y759aFxdwHPOWc+8w59yPQE198dwIw\ns3zALcAA59zXzrlFQB+gsZk1CMVUxxf/fZ1zPzjn5gL3ADeZWfEMOC8RCZBzjqE/DKVDlQ6UyV8m\n6HQkg/S5rA/1S9bnni/uITEpMeh05AwdTjrMzR/fzLS10/i066fHTHdYuXBlxncZz8z1Mxnw5YDg\nkhQREcmkMqI47wD8YGZjzWyrmS00s1uTN5pZeaA4MCN5nXNuNzAfaBRaVQ+ITBHzE7AxLKYhsDNU\nuCebDjjg8nQ/KxEJ1JxNc1i6dSn96/cPOhXJQBEWwWvXvMbiLYt5c8GbQacjZyDJJdFnQh8+WfkJ\nY28YS+tKrY+LaVG+Ba+1fY3Xvn+N179/PYAsRUREMq+MKM4rAHcCPwFXA68Dr5hZj9D24vgCemuK\nz20NbQOIARJCRXtqMcWBbeEbnXOJwI6wGBE5Twz9fiiVC1XmqgpXBZ2KZLAGFzWg72V9efSrR9m+\nf3vQ6chpcM5xx2d3MHrZaEZdP4prq12bamy/ev24t8G93PPFPUxfO/0cZikiIpK5ZURxHgEscM79\n3Tm3xDn3FvAWoHmPRCRNtu7dyrgV47iz3p1EmCaZuBD8q+W/AGgxvAXLty0POBs5mQOHD9B3Yl/e\nWvgW73Z8l641u57yMy+2fpGrKlzFjR/dyM9//HwOshQREcn8IjNgn/8DVqZYtxK4PvTzFsDwo+Ph\no+cxwKKwmCgzy5di9DwmtC05JmX39mxAobCYExowYAD58+c/Zl1sbCyxsbEn+5iIBOTthW8TGRFJ\n70t7B52KnCPFchdjdp/Z3DTuJuq9VY+XW79Mv7r9MLOgU5Mw63et54axN/Djth8Z3mk4PS/peVqf\ni4yIZMwNY2j0TiPaj27PV72+olS+UhmcrYiISMaLi4sjLi7umHXx8fGn9VlzzqVrMmY2CijlnGsW\ntu5loL5z7orQ+83A8865l0Pv8+EL9Z7OuY9C738HbnLOfRKKqYov8hs6574zs2rAcqBe8nPnZnY1\n8Hno+McV6KHp3BYsWLCAOnXqpNwsIpnQ4aTDlP9PeVpXbM3bHd8OOh05x/489CcPTH2A1394nU7V\nOvF2h7cpnKtw0GkJ8OXqL7n545vJF52PcTeOo27Jume8jzU71nDliCtJTEpkcrfJXFL8kgzIVERE\nJFgLFy6kbt26AHWdcwtTi8uI+0NfBhqa2V/NrKKZdQNuBV4LixkM/M3MOphZLWAE8CswAY40iHsH\neMnMmptZXeBdYI5z7rtQzCpgCvCWmdU3s8bAq0DciQpzEcmaPvv5M37d/asawV2gcmbPydB2Q/mk\n6yfM3jCbS964hFnrZwWd1gUtySXx5NdPcs2oa2hYqiELbl+QpsIcoGKhivy3738plrsYTd5rwtQ1\nU9M5WxERkawj3Ytz59wPwHVALLAMeBS4zzn3YVjMc/hCehi+S3tOoK1zLiFsVwOAz4BxwCxgM37O\n83DdgFX4Lu2fAbMBTYAsch4Z8v0QGpZqSJ0SutvlQtapWieW3LGEyoUrc+XwK3l0xqMcSjwUdFpZ\nzrZ925izcQ4HDh9I0+d3/LmD9qPb8/isx3mi+RNMip1EoZyFziqnEnlLMLvPbK4ocwXtRrfjvUXv\nndX+REREsqqMeOYc59zn+NvLTxbzOPD4SbYfxM9bfs9JYnYB3dOUpIhkej9t/4npa6fzwXUfBJ2K\nZAKl8pVieo/pPDvnWf4x8x8s27aMT7p+QraIbEGnluk451i/az2Ltixi0f8W+dcti9i8ZzPgf5eP\nNXuM3pf2JjLi9P4UmLNxDt0/6c7ug7v54uYvTjhVWlrlicrDxNiJ3DX5Lm6ZeAvrd63n8eaPq8eA\niIhcUDKkOBcRSQ+v//A6RXIV4YaLbwg6FckkskVk45Emj3Bp8UvpENeBQdMH8fzVzwedVqYyd9Nc\nrhtzHdv2+dlGY3LHcFmJy+h1SS8uK34ZpfKV4pXvXuG2Sbfx3JzneLLFk3Sp0eWEMyHs/HMno5aN\n4p1F77B4y2LqlazHrF6zKFugbLrnHRkRyRvt36BcgXI88tUjbIjfwJsd3iQqW1S6H0tERCQzUnEu\nIpnSvoR9vL/4fe6sdyc5InMEnY5kMtdUvoaXrn6J+6fcT7Ui1ehbp2/QKWUKG+M3ct2Y66hUqBLv\nXfselxW/jBJ5SxwX16h0IwY1HsTfZv6N2PGxPPPtMzx95dO0q9wOh2Pmupm8s+gdPl75MYkukQ5V\nOvDPFv+kdaXWpz3SnhZmxl+b/JWyBcrS+9Pe/Lr7V15u/TK1Ympl2DFFREQyCxXnIpIpjV42mt0H\nd9OvntpIyInde/m9rNy+kjsn30mlQpVoVq7ZqT90Htt/aD+dPuxEzsicfNr1U4rmLnrS+EuKX8Kk\n2EnM3TSXR2Y8Qoe4DtQvWZ/t+7ezbtc6qhauylMtnqLnJT2JyRNzjs7C61arGxflvYiu47pS+43a\n1C1Rl96X9ia2Zqy69YuIyHkr3adSy8w0lZpI1pCQmECNoTW4uOjFTLhpQtDpSCZ2KPEQbUa1YcmW\nJcy/dT4VC1UMOqVAOOeIHR/LpJ8nMfeWuWc8JZlzjulrp/P83Ocpla8UfS/ry19K/yXwZ74TEhP4\n/JfPeX/x+0z+ZTIRFkHHqh3pfUnvDB/FFxERSS+nO5WainMRyXQG/3cwD0x9gCV3LKFmsZpBpyOZ\n3I4/d3D525eTPSI78/rOI3+O/EGndM7965t/8ehXjzLuxnF0vjjlxCbnh237tjFq6SjeW/wey7Yt\no3ie4jS4qAEVClSgYqGKVChYgQoFK1C+QHmiI6ODTldEROQIFecnoOJcJPPb8ecOKr1SiS41uvBG\n+zeCTkeyiJ+2/8Tlb19Ow1IN+azbZxfUiOrEnyZy7YfX8lizx3i8+eNBp5PhnHMs2rKI0ctGs+L3\nFazZuYb1u9aTkOhnYzWMi/JdRGzNWJ5o/gQ5s+cMOGMREbnQnW5xfuH89SIiWcKTXz/J4aTDPNH8\niaBTkSykapGqfHTjR7Qd1Zb/m/p/DG4zOOiUzonl25Zz88c3c3316/lHs38Enc45YWbUKVGHOiWO\nfsmemJTI5j2bWbtzLWt2rmHZ1mW8Mv8VPl31Ke9e+y5XlLkiwIxFREROj4pzEck0fv7jZ4Z8P4Sn\nWjx1zhtQSdbXqmIrXmn7Cnd9fhcAdUvUJSZPDMXzFKd4nuIUzln4vJoT/Y/9f9Dxw45UKFiB4Z2G\nn3AqtAtFtohslM5fmtL5Sx9pDNivXj9umXALTd9ryj0N7uFfLf9F7qjcAWcqIiKSOhXnIpJpPDTt\nIS7KexH3N7w/6FQki+pfvz+b4jcx5Psh/Gf+f47Zls2yUTR30SPFevE8xYnJHXPM+9L5SlOhYIXA\nG6GdyqHEQ3QZ14XdB3czo+cM8kTlCTqlTKdakWp80+cbXpn/Co9+9SiTfp7EOx3foUX5FkGnJiIi\nckJ65lxEMoWZ62Zy5Ygr+bDzh3St2TXodOQ88OehP9m6bytb9m5hy94tbN0b9nPY+i17t/Dn4T+P\nfK5EnhJcVeEqWpZvScsKLSmVr1SAZ3G8XQd20W18N6atncaMnjNoWrZp0Clleqt3rKbvxL7M3jCb\nO+rewXOtniNvdN6g0xIRkQuEGsKdgIpzkcwpMSmRem/VI0dkDubeMjfTj1rK+cU5x96EvWzZu4U1\nO9fw1bqvmL52Oou3LMbhqFq46pFCvVnZZoHOs73i9xV0+rAT2/dvZ8wNY2hVsVVguWQ1SS6J179/\nnYenP0ypfKX44uYvKF+wfNBpiYjIBUAN4UQkyxixZASLtyxmXt95KszlnDMz8kbnJW90XioXrkyb\nSm0A2L5/OzPXzWTGuhlMWTOFoT8MBaBmsZo0LdOUpmWb0qRsE0rmLXlO8pywagI9PulB2QJl+f62\n7y/YOd3TKsIiuKvBXVxd8WrajmpLo3caMbnbZOqWrBt0aiIiIoBGzkUkYHsT9lLl1So0K9eMuM5x\nQacjkqoNuzYwe8Nsv2yczc9//AxApUKVuKLMFRTMUZDEpEQOJx0m0SWSmJToX10iNYvWpEuNLpQt\nUPaMj5vkkvjn7H/y2KzHuL769QzvNFzPmJ+lbfu20SGuA8u3Lfdd/iu3DTolERE5j+m29hNQcS6S\n+Tw28zGenfMsP939U5oKF5GgbNm7hW82fMPsDbOZ++tc/jz0J9kispHNshEZEXnkZ4BFWxZx4PAB\nGpZqSNcaXbnx4hu5KN9FpzzGnoN76PVpLz5Z9QlPNn+SR5s+ekF3ZU9P+xL2ETs+ls9/+Zxh7YfR\nt07foFMSEZHzlG5rF5FM79fdv/L83OcZ2GigCnPJcornKc6NNW7kxho3njJ2z8E9TPxpImOWj+Gh\naQ8xcMpAmpRtQtcaXalTog4HDx/kYOLBY14PHD7Ay/99mY3xG5lw0wQ6Vu14Ds7qwpE7Kjcfd/2Y\nez6/h1sn3cqm3Zt4rNljerRGREQCo+JcRALz95l/J290XgZdMSjoVEQyVN7ovNxc+2Zurn0zuw7s\n4tNVnzJm+Rju+/I+DicdTvVzFxe9mPm3zqd60ernMNsLR2REJEPbDaVsgbL8dcZf2Ri/kWHth5E9\nW/agUxMRkQuQinMRCcThpMOMWzGOQY0HkS86X9DpiJwzBXIUoPelvel9aW92/LmD33b/RnRkNNHZ\noo97zRaRLeh0z3tmxqArBlEqXylumXALv+z4hX80/QdXVbhKo+giInJOqTgXkUD8uO1H9ibs1RzN\nckErlLMQhXIWCjoNAbrX7k6pfKUYOGUgV4+8mlrFajGw0UBia8YSHRkddHoiInIBUFcZEQnEvE3z\niIyIpF7JekGnIiICQPNyzVlw+wJm9ppJ2QJl6TOhD2UHl+Wfs//JH/v/CDo9ERE5z6k4F5FAzP11\nLpcVv4yc2XMGnYqIyBFmRvNyzZkUO4lVd62iU7VOPP3N05R+uTT9J/dn3c51QacoIiLnqQwvzs1s\nkJklmdlLKdY/aWabzWy/mU0zs0optkeb2RAz225me8xsnJkVSxFT0MxGmVm8me00s7fNLHdGn5OI\nnL25m+byl9J/CToNEZFUVS1SlTfav8GmAZt4pMkjjFsxjsqvVqbPhD788scvQacnIiLnmQwtzs2s\nPnA7sCTF+oeBu0PbGgD7gClmFhUWNhhoB3QGmgIlgfEpDjEaqA60DMU2BYal+4mISLrauncra3eu\nVXEuIllCkVxF+FvTv7HuvnU83+p5pqyeQrUh1bj545tZvm150OmJiMh5IsOKczPLA4wE/p+9+w6v\nosr/OP4+6YWEAKEmNAEhFJUiBFAUVFBRETsIKPYGimVFV3etq2vDn6issroWJIiiq2IBFEF6CSIg\nRSmhhA6BhCSknt8f5yZ7CQkkIeES+LyeZ55778x3Zs7cDCHfOe02YF+RzfcDz1prJ1trVwBDcMn3\nlZ59I4FbgBHW2pnW2l+BoUB3Y0xnT0wc0Ae41Vq72Fo7FxgG3GCMqVdZ1yUix27elnkASs5FpEoJ\nDwpnRNcRrL9/PaMvGc2sjbNoO6Yt10y8hqXbl/q6eCIiUsVVZs35W8A31trp3iuNMU2BesBPBeus\ntanAAqCrZ1Un3Ejy3jFrgE1eMfFAiidxL/AjYIEuFXolIlKh5m6eS2xkLLGRsb4uiohImYUEhHDP\n2fewdvhaxl4+ll+3/0r7d9pz0ccX8d2f35Fv831dRBERqYIqJTk3xtwAnAU8VszmergEekeR9Ts8\n2wDqAtmepL2kmHrATu+N1to8YK9XjIicgNTfXEROBkH+QdzW4TbW3LeG8VeNZ//B/fQd35fWb7Vm\nzKIxpGen+7qIIiJShVR4cm6MicX1F7/RWptT0ccXkaotOy+bxVsX0y1WybmInBwC/AIY0G4AC25b\nwOyhs2lXtx33fX8fDUc1ZOSPI9mSusXXRRQRkSogoBKO2RGoDSwxxhjPOn+ghzHmPqAVYHC14961\n53WBgibq24EgY0xkkdrzup5tBTFFR2/3B2p6xRRrxIgRVK9e/ZB1AwYMYMCAAaW6QBEpv1+3/UpW\nXhZdG3Y9erCISBVijKF7o+50b9SdpH1JjF4wmjGLx/DK3Ffo3qg7fZr1oXez3nSo3wE/o9lsRURO\nRgkJCSQkJByybv/+/aXa11hrK7QwnqnMGhdZ/QGwCnjRWrvKGLMVeNlaO8qzTyQuUR9irf3M83kX\ncIO19ktPTEvPMeKttQuNMa2A34FOBf3OjTG9ge+AWGvtYQm6MaYDkJiYmEiHDh0q9LrwRCb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yt9XZzjKt/msyt9F8lpySxMXsjr819nzZ41xMfG82D8g/SP63/IoHi70nfxwuwXeHvR20QGR/JE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\nfrq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xUE4OHyktZXx2NuOzszli0CAKDmCcUjvVHeks1R3pLNUd6SzVHeks1Z2u67PJOVBC\nMOlfVYftVcCEng9H+ru+8oXzl3BZlijBUhCJE2pECbqD/ddMmOFzHHbN0Jq41mr7ep9nDB5M1gFM\naBZ356fr1u2aKKN9qZfEZV9aE5dq6bBsS1s4nqjjDJwdJwFJlGFGUVoahQmPkvT0XcuYFKSl7Zr0\nJjvhkRVOrDIyXBanO1vE+0rdkd5HdUc6S3VHOkt1RzpLdafr+nJy3hlZAEuW7M9ceSK7a21tZd68\nvc7f0Gtcu2QJC8MJyrrb36dNozg9fb/Luzvfe/11MjvMCJsVzjJaFInstoxFezrcviRJxIys9tlV\nwxlWM8Pn9lkw86NR8hKWbMnsePMgFgse+7F8DMDm8NGd+krdkd5HdUc6S3VHOkt1RzpLdWfvEvLP\nrH2V67OztYfd2huAs9398YTt9wMF7n7WHt5zHvBQjwUpIiIiIiIiEjjf3X+zt519tuXc3VvNbC5w\nMvA4gAVTNZ4M3LKXtz0NnA+sBpp6IEwREREREREZ2LKA0QT56F712ZZzADP7OHA/cDnwCsHs7R8F\nJrr7lhSGJiIiIiIiIrLf+mzLOYC7/z5c0/w7QBnwOnCqEnMRERERERHpS/p0y7mIiIiIiIhIfxBJ\ndQAiIiIiIiIiA52ScxEREREREZEUU3IuIiIiIiIiexWuiiVJ1i+Tc1Ue6SzVHeks1R3pLNUd6QrV\nH+ks1R3ZX+EE3LkJP6vuJEmfnxDOzDKALwC1wOvu/kqKQ5I+QnVHOkt1RzpLdUe6QvVHOkt1RzrD\nzNKAu4CTgC0EK2N9xd13pjKu/qxPJ+dmdjrwK+BtIA8oBW529x+mNDDp9VR3pLNUd6SzVHekK1R/\npLNUd6QzwsT8QWA08HXgROB8YA1wkbtvSF10/VdfT84fBra4++fMrAI4DbgHuAT4jbu3pDRA6bVU\nd6SzVHeks1R3pCtUf6SzVHekM8xsJPB34FvuPjvcNhpYCNwK/Njdd6QswH6qz445N7OxwDHAswDu\nvsnd7wMeAK4EDk9heNKLmdkYVHekE1R3pLNUd+RAmVk04bWueaRTVHekC4qBEcBLAGaW6e6rge8B\nM4EjUxda/9VnknMz+x8zm2pm7TGvAjKAonB/drj9aqACOD0cXyMDnJmN6zBxxRpUd2Q/mFl5h02q\nO7JfzOxCMxufsEl1R/abmX0P+H7CJl3zyH7R9bJ0hpl9zcy+bmZnJGxeClQBnwx/jgO4+w1AG3BG\n+F5NDteNen1ybmYXmdkm4KfA88CtZjbMg/74jwGXA7h7o5mlu/tW4G7gIiCWorClFzCzS8xsLfAE\n8KKZfQzA3ePAH1Hdkb0ws0vNbB7wmJk9bmbvDndlAI+iuiN7YWYRM3sUuB8408yywl1RVHfkHZjZ\nGWZWCbwP2GhmxeGuCKo/sg+6XpbOMLOjw2vljwEnAw+a2QNmNgRoBh4BzjWzIe7emnBz5ybgXADv\ny2Oke6FenZyb2acIJiCYBbyX4IvlkwQTWQD8Dcg0s6vCn9vP536gBHXVGbDM7GzgOuAbBHeH5wMP\nmNn5YZEngGzVHUlkZuVm9juCunM7QX0w4LcA7t4EPIO+d2QPzCwS3vzbALwGfAmYBODurQRj9zJU\nd2RPzCwX+BRwu7sf5e63uPt2AHePoe8e2QtdL0sXnEMwe/8M4HTgVOCDwJeBbIKbgjuBb4blm8Ln\njUCjmU3s2XD7v16ZnIctD1GCL5g57v5bd9/q7r8hqAztcb9A8IXzJTOrcPfmcPtUYCtBZZIBJKFr\nzfHAGnd/0N3/7O6fI7j7d4WZnQj8A3ga1R3Z3fHAcOA0d7/X3e8iuNhpNrMzwzLPoboje+Du8XAt\n2GOBU4BGgu+c9gvkOQQJuuqO7MlJBGODv29mRWZ2vZldk3BT+RlUfySBBXS9LAcsrDsFBOPGF4eb\nm939JeBHwPuBD7v7HODXwCfDLu9pYdnpwFJ3X9rDofd7vSo5b+8q4e7x8C7xIQQXxWXh/lsIxjt8\n2MyOdfcq4GfAeuBJMzvHzMYRTFLwJrA6BachKZTQtWYysAR2re0JwV3lKEH9ALgNWIfqjvzHYuA2\nd1+csC1O0J29CsDdNwP3EowhVt2RXcKL5J1Affh8LXAhMDIssh24Ef3NkgQJN5XLCJKo9wBzgcOA\n4wh6fd1KcM2m+iO7eCBG0ENH18uyT2Y2Leyh0153aghax/PDIu3XyzcT3LQ5IxxacydwB8Gyak+F\nPQyvBh4Oj6sx592oVyylZmbnApcBtcC/gEfdfX24/QaCCQmmA1uA3xD84RpMsPzDj8IvowcIZhQs\nJPiSOT+cUVD6sXAc+fuARcA/3f2NcPvXgSvcvSL8Oc3d28zsaoLxVRe6+9yw7txPcPGsujOAdKg7\n/3L3hR32G0GX9snAX4D3uPvyhP2qOwPU3r53wn3jCFo4D3f3HWb2T4KkqxC4yd1/orozsO3j79a5\nwM+BhwgujL8XjvH8JPBZ4GF3/6nqz8D1DnVH18uyR+FQz5sJxpA7QQPDL9x9u5l9gWDyyTJ3bzCz\nDHdvCXvs3Ehw7bM4PM5HgSlAOcHfM7WaJ0FKk3MzKyS4G3MSQSvmKIIvnZ+4+21hmRLgYoLxDx90\n97pw+y8Ixslc7u6bw0l3CoHSxAsl6Z/MbDDBRcwJBJO7HQqMAb7u7g+Y2SEELRDfdvebLVj+oTls\nRd9CkLj/KjxWJsEspqo7A8A71Z2wTBSIu7ub2aXAZe5+tJlZuC0SdmHW984Asp91573AJe5+gQWz\ntT8alnsM+JSHa8Lqe2fg2Uf9+Ya73x/eEFxIcEPwEne/P3yfEbRQ7QA+F144q/4MIO9Ud8IyZQQ9\ndXS9LLuY2VEEN2TuBF4E3k0wfvw24AcEN2/+ATzj7pdbMFlga/jeHcBnPVzjXHpG2jsXSaojCb5g\nTnD3FQBm9hJhF0ALloHYRnAX8O/uXtd+RweoIxhjUx8eq9ndK4HKHj4HSY33ENSTI9x9A4CZPQJ8\n08y2uvufzeyXwLfM7JfuXhMmVi1mtpKg3gEQjr1S3Rk49lZ3rjOzGnf/I2GPr7D8hwj+oBEm5iMI\nWrUa0ffOQLOvurPT3f8A5ABHmNmDBDPZ3kUwXGIKweRMO8JjtajuDDj7qj873P2PZnYHwWSUOe09\nvsLvnTrgkPD6B1R/Bpq91Z1vmFmtuz+KrpclQXtjAkGdyAbuc/edwGthQ9VZwDp3v9PMbgRuMbOH\n3P258P2HEPy9qknRKQxYKRlznjA24V1AA8Faee02AM+Z2SDYNYZ4MDAj/LklvDt4MPBbd69PKCcD\nx3nAenffYGZ54bbHgdEEE54UEizzsA74nZkNDy9wDiG4eH4iFUFLr7C3ujMWuNLMSsJW8XQzKyKY\nMOcJM8s2s5sJxpqfBPreGYD2VXc+F9aXemAQwfrB73X3KwlasyYBnzKzdFDdGaD2Vn/GEHz3DCa4\nmfN34EqChAozKweGAve1H0j1Z8DZ13fP582szN3b0PWyhBJ+z2OAt9h9ubzbwm0fMbPRBN87vyW4\nXv66mU0FriKYJ2VuT8UsgR5Lzs3sRDM7NbwT3F5hlhB0tfm+BWtSryJYY+9HBGP2PhuWux74gJm9\nEN5Vfo1g8oJf9FT8kjqJdSdh89sEXf8I7wRCcPH7T4I7hB9x93UE6zZOAv7PggksniUYk7Uo4SaR\n9FOdqDtZBHeT25e+Gg20AicCq8LnY939qR45AUmZTtSdHOADBL0sziCY5fb58G9eC8F30f3t3QWl\nf+vC3604cD6wGfiNmf0FeB1IB/7cU/FL6nTy79YZ4bYfouvlAcnM/sfMbjGzL4Zd2du9QNAYWh6W\ni4ZDHh4mGBpzkrvH3P0CglWNziYYjnUE8EkPJhOUHpT0MefhmPGfAp8gGEt1ZuLEE2Z2IcFdnU8R\nTIJyM8FYmEsJxp/PdPfFZvYR4CiCSSz+6O4PJzVwSbl91R0zGwu8RHCD50mChKqcoN7cSNAy/s2w\nBXQSwZ3kI4AX3f33PXwq0sO6WHced/f/F5a9iqAHxlqC+Qt+2bNnIj2tq9877n5dh+OZJ/sPrfQa\nXf3uIfieiZnZEGBa+FgeDreRfqy7vnssWPbzGHS9PCCYWQXBzZejCHrdTCUYAvE+d38lnGNgPvBs\nOKY86sEM/5jZ6wTDIK4Of44Q3Cgc4ZrsLWWS2nIe3vX7GMFMtecCBwHn2n+WtsLdHyRoJa8DbnX3\nKndfBvyV4G7fsLDco+7+NXc/X180/d8+6k4mgLuvJPjj9Fa4fy5wlLv/k+CP2qSwBQJ3X+Luv3b3\nLyox7/+6oe4cknC4OcCV7j5aiXn/1x3fOx2PqcR84OiO7572i2Z33+zuf3f3nygx7/+687vH3f+o\n6+WBwcxyCHpL1APHuPsF7j4VWMZ/eh+3Ekz8dpmZHdf+HRNawe7XPO7u9UrMUyupE8J5sHTVPIJx\nMk+Y2UTgSwSJ9+sJRU8Atrj7xoRt2QRrNC5HBpz9qTvu/gLwQsKkJ4StDYcTjJ3Bwlm1U3ISkhLd\nVXfCcq8Ar/T0OUhq6HtHukL1RzpLdUc6w4Olz5oJ5hRYFQ6jaiNY/vW0sEzMzH5PcHPnbjO73N2f\nC1vcRxP0Dmw/nm4m9wI90a19ty59ZraBoEvOV/w/yzxMI+hy8V2C8TMjgO8BfwKuIZhZUhVmgHmn\nupO4P+y2EyPo4nUZwTgZLREyQKnuSGep7khXqP5IZ6nuSGfY7kuftS/z+hBQ7+6fbq83YZ15iqCX\nxXyC7u+rgY97uAKA9A49ts65/WdR+48BvwFOd/e/J+y/jmCcTSuQCdzg7prAQvan7gwDPgxcQjBz\n6RWuNRkF1R3pPNUd6QrVH+ks1R3pKjN7Hrjb3R8wMwMiYQt6GUFSfjSwyt0fSmmgskc9lpzv9qFm\nLxKMj7jA3avMrNjdt5tZLjDB3ef1eFDSJyTUnfPdfbOZlbr7FjObCQx195+mOETppVR3pLNUd6Qr\nVH+ks1R35EBZMHngi8AH3P9/e3cQYld5xQH8f5pEqZGSRdWFShapbWkCEd2JRQpKoUSFdlMpJgst\nVkpLFRURTMBFIaXQTcUsmtJsCqJCIKva7AuBaEzSVUORFFoIhUIRkk16XNw35RGamrxx5svk/X4w\nMO/ed987Fw4z/LnnfrdPzrb991YIbnzrGs5X7oWoqp1JPsp0P82OJA8l+dFKE8GV/k/vPJxpnOvs\n0AK5YekdFqV3WA39w6L0Dtdrbnx9b5L93f2V2fYDmVb2P9DdF4YWyTUZcuU8SarqRKZHW51P8lx3\n/2FIIWw4eodF6R0WpXdYDf3DovQO16Oqfp1p2uJ4pkes3Zbk6e5+f2hhXLM1fZTa/1JVO6rqTJKd\nSX7Y0yOK/KHhM+kdFqV3WJTeYTX0D4vSO1yv2aJv307ycpJjSQ51912C+cay7uE80+qS7yX5cncf\nHvD9bFx6h0XpHRald1gN/cOi9A7XpbsvZVqB/VCSbd19cGxFLGLYWDsAAACfj6ra1N2XR9fB4oRz\nAAAAGGzEWDsAAAAwRzgHAACAwYRzAAAAGEw4BwAAgMGEcwAAABhMOAcAAIDBhHMAAAAYTDgHAACA\nwYRzAAAAGEw4BwDWVFV9oapqdB0AcCMTzgFgiVTV01X1z6racsX2o1V1ZPb7k1V1sqouVtW5qtpf\nVZvm3vtCVZ2uqk+q6nxVvVlVW+f276uqf1XV41X15ySXkty7XucIABuRcA4Ay+WdTP//n1jZUFV3\nJPlOksNV9c0kR5L8KsnXkzyXZF+S1+Y+43KSnyT5RpK9Sb6V5OAV33NbkleSPJNkZ5ILa3AuAHDT\nqO4eXQMAsI6q6s0k27t7z+z1i0me7+77quqPSY5398G59/8gyS+6++6rfN73krzV3XfOXu9L8tsk\nu7v77BqfDgDcFIRzAFgyVXV/khOZAvo/quqjJG9398+r6kKSrUn+M3fIpiS3JLm9uy9V1aNJXs10\nZf1LSTYnuTXJ1tn+fUkOdfcX1/G0AGBDM9YOAEumu08lOZ1kb1U9kGk8/Xez3bcnOZBk99zPriRf\nnQXv7UmOJTmV5LtJHkjy49mxt8x9zcU1Pg0AuKlsHl0AADDEb5L8LMk9mcbY/z7b/kGSr3X3X69y\n3IOZJu9eWtlQVd9f00oBYAkI5wCwnH6f5JdJns20qNuKN5Icq6q/JXk303j77iS7uvv1JOeSbKmq\nn2a6gv5wpkXjAIBVMNYOAEuou/+d5L0knyQ5Orf9/SR7kjyW6b70P2W6wv7xbP/pJC9mWon9TJKn\nMt1/DgCsggXhAGBJVdXxJGe6+4XRtQDAsjPWDgBLpqq2ZXo2+SNJnh9cDgAQ4RwAltGHSbYleaW7\n/zK6GADAWDsAAAAMZ0E4AAAAGEw4BwAAgMGEcwAAABhMOAcAAIDBhHMAAAAYTDgHAACAwYRzAAAA\nGEw4BwAAgMGEcwAAABjsUzYvTdcLEUtKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1140cbcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset.plot(subplots=True, figsize=(12, 10), grid=False,\n",
    "            title=\"Number of births per year\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 评价命名多样性的增长\n",
    "\n",
    "上图反应的降低情况可能意味着父母愿意给小孩起常见的名字越来越少。这个假设可以从数据中得到验证。一个办法是计算最流行的1000个名字所占的比例，我按year和sex进行聚合并绘图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table = top1000.pivot_table('prop', index='year',\n",
    "                           columns='sex', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116c8c128>"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7Dp4IBmi2ZBPwQzMbhB8b8WX8IMpr3b4LnT2F7/6aip+p1Jbm/Rfw428uBl50EWu3BB7H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\nUyEhIiIiif0/yke6VU1+1JgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1140cbb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.plot(title='Sum of table1000.prop by year and sex',\n",
    "           yticks=np.linspace(0, 1.2, 13), xticks=range(1880, 2020, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看出，名字的多样性确实出现了增长（前1000项的比例降低）。另一个办法是计算占总出生人数前50%的不同名字的数量，这个数字不太好计算。我们只考虑2010年男孩的名字："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = boys[boys.year == 2010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>260877</th>\n",
       "      <td>Jacob</td>\n",
       "      <td>M</td>\n",
       "      <td>21875</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.011523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260878</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>M</td>\n",
       "      <td>17866</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260879</th>\n",
       "      <td>Michael</td>\n",
       "      <td>M</td>\n",
       "      <td>17133</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260880</th>\n",
       "      <td>Jayden</td>\n",
       "      <td>M</td>\n",
       "      <td>17030</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260881</th>\n",
       "      <td>William</td>\n",
       "      <td>M</td>\n",
       "      <td>16870</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name sex  births  year      prop\n",
       "260877    Jacob   M   21875  2010  0.011523\n",
       "260878    Ethan   M   17866  2010  0.009411\n",
       "260879  Michael   M   17133  2010  0.009025\n",
       "260880   Jayden   M   17030  2010  0.008971\n",
       "260881  William   M   16870  2010  0.008887"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对prop降序排列后，我们想知道前面多少个名字的人数加起来才够50%。虽然编写一个for循环也能达到目的，但NumPy有一种更聪明的矢量方式。先计算prop的累计和cumsum，，然后再通过searchsorted方法找出0.5应该被插入在哪个位置才能保证不破坏顺序："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prop_cumsum = df.sort_values(by='prop', ascending=False).prop.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260877    0.011523\n",
       "260878    0.020934\n",
       "260879    0.029959\n",
       "260880    0.038930\n",
       "260881    0.047817\n",
       "260882    0.056579\n",
       "260883    0.065155\n",
       "260884    0.073414\n",
       "260885    0.081528\n",
       "260886    0.089621\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([116])"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum.searchsorted(0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于数组索引是从0开始的，因此我们要给这个结果加1，即最终结果为117.拿1900年的数据来做个比较，这个数字要小得多："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41853    0.979223\n",
       "41852    0.979277\n",
       "41851    0.979330\n",
       "41850    0.979383\n",
       "41849    0.979436\n",
       "41848    0.979489\n",
       "41847    0.979542\n",
       "41846    0.979595\n",
       "41845    0.979648\n",
       "41876    0.979702\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = boys[boys.year == 1900]\n",
    "in1900 = df.sort_values(by='prop', ascending=False).prop.cumsum()\n",
    "in1900[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25])"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in1900.searchsorted(0.5) + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在就可以对所有year/sex组合执行这个计算了。按这两个字段进行groupby处理，然后用一个函数计算各分组的这个值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_quantile_count(group, q=0.5):\n",
    "    group = group.sort_values(by='prop', ascending=False)\n",
    "    return group.prop.cumsum().searchsorted(q) + 1\n",
    "\n",
    "diversity = top1000.groupby(['year', 'sex']).apply(get_quantile_count)\n",
    "diversity = diversity.unstack('sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，这个diversity有两个时间序列（每个性别各一个，按年度索引）。通过IPython，可以看到其内容，还可以绘制图标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>[38]</td>\n",
       "      <td>[14]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>[38]</td>\n",
       "      <td>[14]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>[38]</td>\n",
       "      <td>[15]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>[39]</td>\n",
       "      <td>[15]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>[39]</td>\n",
       "      <td>[16]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex      F     M\n",
       "year            \n",
       "1880  [38]  [14]\n",
       "1881  [38]  [14]\n",
       "1882  [38]  [15]\n",
       "1883  [39]  [15]\n",
       "1884  [39]  [16]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11cc87160>"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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yLddDEkPjTwcBn7ojx3NPwTcF/Aichl9hbbKZXeh8BlINOBCeEIRsIsO4eREREck/eZmn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\nc77affZsXy5dgwZ+qFf4F1p6Vf3evdC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Z//l67n7j7h0+WvrxFY/TuLWRGaNnMGnIJO55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\nvLrdxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11875acc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diversity=diversity.astype(float)\n",
    "\n",
    "diversity.plot(title='Number of popular names in top 50%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>38.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>38.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>38.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>39.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>39.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex      F     M\n",
       "year            \n",
       "1880  38.0  14.0\n",
       "1881  38.0  14.0\n",
       "1882  38.0  15.0\n",
       "1883  39.0  15.0\n",
       "1884  39.0  16.0"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 注意，如果不加`diversity=diversity.astype(float)`的话，会报错显示，“no numeric data to plot” error。通过加上这句来更改数据类型，就能正常绘图了。\n",
    "\n",
    "从图中可以看出，女孩名字的多样性总是比男孩高，而且还变得越来越高。我们可以自己分析一下具体是什么在驱动这个多样性（比如拼写形式的变化）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## “最后一个字母”的变革\n",
    "\n",
    "一位研究人员指出：近百年来，男孩名字在最后一个字母上的分布发生了显著的变化。为了了解具体的情况，我们首先将全部出生数据在年度、性别以及末字母上进行了聚合："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 从name列中取出最后一个字母\n",
    "get_last_letter = lambda x: x[-1]\n",
    "last_letters = names.name.map(get_last_letter)\n",
    "last_letters.name = 'last_letter'\n",
    "\n",
    "table = names.pivot_table('births', index=last_letters,\n",
    "                          columns=['sex', 'year'], aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "0    y\n",
      "1    a\n",
      "2    a\n",
      "3    h\n",
      "4    e\n",
      "Name: last_letter, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(type(last_letters))\n",
    "print(last_letters[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后，我选出具有一个代表性的三年，并输出前几行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>108376.0</td>\n",
       "      <td>691247.0</td>\n",
       "      <td>670605.0</td>\n",
       "      <td>977.0</td>\n",
       "      <td>5204.0</td>\n",
       "      <td>28438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>694.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>3912.0</td>\n",
       "      <td>38859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>15476.0</td>\n",
       "      <td>23125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6750.0</td>\n",
       "      <td>3729.0</td>\n",
       "      <td>2607.0</td>\n",
       "      <td>22111.0</td>\n",
       "      <td>262112.0</td>\n",
       "      <td>44398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>133569.0</td>\n",
       "      <td>435013.0</td>\n",
       "      <td>313833.0</td>\n",
       "      <td>28655.0</td>\n",
       "      <td>178823.0</td>\n",
       "      <td>129012.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                            M                    \n",
       "year             1910      1960      2010     1910      1960      2010\n",
       "last_letter                                                           \n",
       "a            108376.0  691247.0  670605.0    977.0    5204.0   28438.0\n",
       "b                 NaN     694.0     450.0    411.0    3912.0   38859.0\n",
       "c                 5.0      49.0     946.0    482.0   15476.0   23125.0\n",
       "d              6750.0    3729.0    2607.0  22111.0  262112.0   44398.0\n",
       "e            133569.0  435013.0  313833.0  28655.0  178823.0  129012.0"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable = table.reindex(columns=[1910, 1960, 2010], level='year')\n",
    "subtable.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来我们需要安总出生数对该表进行规范化处理，一遍计算出个性别各末字母站总出生人数的比例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex  year\n",
       "F    1910     396416.0\n",
       "     1960    2022062.0\n",
       "     2010    1759010.0\n",
       "M    1910     194198.0\n",
       "     1960    2132588.0\n",
       "     2010    1898382.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.273390</td>\n",
       "      <td>0.341853</td>\n",
       "      <td>0.381240</td>\n",
       "      <td>0.005031</td>\n",
       "      <td>0.002440</td>\n",
       "      <td>0.014980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>0.001834</td>\n",
       "      <td>0.020470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000538</td>\n",
       "      <td>0.002482</td>\n",
       "      <td>0.007257</td>\n",
       "      <td>0.012181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.017028</td>\n",
       "      <td>0.001844</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>0.113858</td>\n",
       "      <td>0.122908</td>\n",
       "      <td>0.023387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.336941</td>\n",
       "      <td>0.215133</td>\n",
       "      <td>0.178415</td>\n",
       "      <td>0.147556</td>\n",
       "      <td>0.083853</td>\n",
       "      <td>0.067959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000783</td>\n",
       "      <td>0.004325</td>\n",
       "      <td>0.001188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.002250</td>\n",
       "      <td>0.009488</td>\n",
       "      <td>0.001404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.051529</td>\n",
       "      <td>0.036224</td>\n",
       "      <td>0.075852</td>\n",
       "      <td>0.045562</td>\n",
       "      <td>0.037907</td>\n",
       "      <td>0.051670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>0.001526</td>\n",
       "      <td>0.039965</td>\n",
       "      <td>0.031734</td>\n",
       "      <td>0.000844</td>\n",
       "      <td>0.000603</td>\n",
       "      <td>0.022628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>0.036581</td>\n",
       "      <td>0.049384</td>\n",
       "      <td>0.018541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l</th>\n",
       "      <td>0.043189</td>\n",
       "      <td>0.033867</td>\n",
       "      <td>0.026356</td>\n",
       "      <td>0.065016</td>\n",
       "      <td>0.104904</td>\n",
       "      <td>0.070367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m</th>\n",
       "      <td>0.001201</td>\n",
       "      <td>0.008613</td>\n",
       "      <td>0.002588</td>\n",
       "      <td>0.058044</td>\n",
       "      <td>0.033827</td>\n",
       "      <td>0.024657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>0.079240</td>\n",
       "      <td>0.130687</td>\n",
       "      <td>0.140210</td>\n",
       "      <td>0.143415</td>\n",
       "      <td>0.152522</td>\n",
       "      <td>0.362771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>o</th>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.002439</td>\n",
       "      <td>0.001243</td>\n",
       "      <td>0.017065</td>\n",
       "      <td>0.012829</td>\n",
       "      <td>0.042681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.003172</td>\n",
       "      <td>0.005675</td>\n",
       "      <td>0.001269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>0.013390</td>\n",
       "      <td>0.006764</td>\n",
       "      <td>0.018025</td>\n",
       "      <td>0.064481</td>\n",
       "      <td>0.031034</td>\n",
       "      <td>0.087477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>0.039042</td>\n",
       "      <td>0.012764</td>\n",
       "      <td>0.013332</td>\n",
       "      <td>0.130815</td>\n",
       "      <td>0.102730</td>\n",
       "      <td>0.065145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.027438</td>\n",
       "      <td>0.015201</td>\n",
       "      <td>0.007830</td>\n",
       "      <td>0.072879</td>\n",
       "      <td>0.065655</td>\n",
       "      <td>0.022861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u</th>\n",
       "      <td>0.000684</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.001221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.001434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.006329</td>\n",
       "      <td>0.007711</td>\n",
       "      <td>0.016148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>0.003965</td>\n",
       "      <td>0.001851</td>\n",
       "      <td>0.008614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.110972</td>\n",
       "      <td>0.152569</td>\n",
       "      <td>0.116828</td>\n",
       "      <td>0.077349</td>\n",
       "      <td>0.160987</td>\n",
       "      <td>0.058168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.002439</td>\n",
       "      <td>0.000659</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.001831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                             M                    \n",
       "year             1910      1960      2010      1910      1960      2010\n",
       "last_letter                                                            \n",
       "a            0.273390  0.341853  0.381240  0.005031  0.002440  0.014980\n",
       "b                 NaN  0.000343  0.000256  0.002116  0.001834  0.020470\n",
       "c            0.000013  0.000024  0.000538  0.002482  0.007257  0.012181\n",
       "d            0.017028  0.001844  0.001482  0.113858  0.122908  0.023387\n",
       "e            0.336941  0.215133  0.178415  0.147556  0.083853  0.067959\n",
       "f                 NaN  0.000010  0.000055  0.000783  0.004325  0.001188\n",
       "g            0.000144  0.000157  0.000374  0.002250  0.009488  0.001404\n",
       "h            0.051529  0.036224  0.075852  0.045562  0.037907  0.051670\n",
       "i            0.001526  0.039965  0.031734  0.000844  0.000603  0.022628\n",
       "j                 NaN       NaN  0.000090       NaN       NaN  0.000769\n",
       "k            0.000121  0.000156  0.000356  0.036581  0.049384  0.018541\n",
       "l            0.043189  0.033867  0.026356  0.065016  0.104904  0.070367\n",
       "m            0.001201  0.008613  0.002588  0.058044  0.033827  0.024657\n",
       "n            0.079240  0.130687  0.140210  0.143415  0.152522  0.362771\n",
       "o            0.001660  0.002439  0.001243  0.017065  0.012829  0.042681\n",
       "p            0.000018  0.000023  0.000020  0.003172  0.005675  0.001269\n",
       "q                 NaN       NaN  0.000030       NaN       NaN  0.000180\n",
       "r            0.013390  0.006764  0.018025  0.064481  0.031034  0.087477\n",
       "s            0.039042  0.012764  0.013332  0.130815  0.102730  0.065145\n",
       "t            0.027438  0.015201  0.007830  0.072879  0.065655  0.022861\n",
       "u            0.000684  0.000574  0.000417  0.000124  0.000057  0.001221\n",
       "v                 NaN  0.000060  0.000117  0.000113  0.000037  0.001434\n",
       "w            0.000020  0.000031  0.001182  0.006329  0.007711  0.016148\n",
       "x            0.000015  0.000037  0.000727  0.003965  0.001851  0.008614\n",
       "y            0.110972  0.152569  0.116828  0.077349  0.160987  0.058168\n",
       "z            0.002439  0.000659  0.000704  0.000170  0.000184  0.001831"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop = subtable / subtable.sum()\n",
    "letter_prop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有了这个字母比例数据后，就可以生成一张各年度各性别的条形图了："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11775a128>"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y1LHsCJMfSZIkqQfGA5PKHUSJ3vOe93QoO+igg3jnO99JfX19S9nSpUs57rjj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\nkiRJkjrB5EeSJElSJ/wG6z3DMrhnoAYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1172afef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, figsize=(10, 8))\n",
    "letter_prop['M'].plot(kind='bar', rot=0, ax=axes[0], title='Male')\n",
    "letter_prop['F'].plot(kind='bar', rot=0, ax=axes[1], title='Femal', legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看出来，从20世纪60年代开始，以字母'n'结尾的男孩名字出现了显著的增长。回到之前创建的那个完整表，按年度和性别对其进行规范化处理，并在男孩名字中选取几个字母，最后进行转置以便将各个列做成一个时间序列："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"10\" halign=\"left\">F</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1880</th>\n",
       "      <th>1881</th>\n",
       "      <th>1882</th>\n",
       "      <th>1883</th>\n",
       "      <th>1884</th>\n",
       "      <th>1885</th>\n",
       "      <th>1886</th>\n",
       "      <th>1887</th>\n",
       "      <th>1888</th>\n",
       "      <th>1889</th>\n",
       "      <th>...</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.345587</td>\n",
       "      <td>0.343440</td>\n",
       "      <td>0.338764</td>\n",
       "      <td>0.341251</td>\n",
       "      <td>0.338550</td>\n",
       "      <td>0.341270</td>\n",
       "      <td>0.339703</td>\n",
       "      <td>0.335258</td>\n",
       "      <td>0.332764</td>\n",
       "      <td>0.328706</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020162</td>\n",
       "      <td>0.020019</td>\n",
       "      <td>0.019177</td>\n",
       "      <td>0.019505</td>\n",
       "      <td>0.018481</td>\n",
       "      <td>0.017635</td>\n",
       "      <td>0.016747</td>\n",
       "      <td>0.016189</td>\n",
       "      <td>0.015927</td>\n",
       "      <td>0.014980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026256</td>\n",
       "      <td>0.025418</td>\n",
       "      <td>0.024368</td>\n",
       "      <td>0.023171</td>\n",
       "      <td>0.021645</td>\n",
       "      <td>0.020778</td>\n",
       "      <td>0.020357</td>\n",
       "      <td>0.019655</td>\n",
       "      <td>0.019693</td>\n",
       "      <td>0.020470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013972</td>\n",
       "      <td>0.014048</td>\n",
       "      <td>0.014042</td>\n",
       "      <td>0.013514</td>\n",
       "      <td>0.013083</td>\n",
       "      <td>0.012991</td>\n",
       "      <td>0.012983</td>\n",
       "      <td>0.012458</td>\n",
       "      <td>0.012186</td>\n",
       "      <td>0.012181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.006693</td>\n",
       "      <td>0.006601</td>\n",
       "      <td>0.006806</td>\n",
       "      <td>0.007211</td>\n",
       "      <td>0.007100</td>\n",
       "      <td>0.006478</td>\n",
       "      <td>0.006967</td>\n",
       "      <td>0.007035</td>\n",
       "      <td>0.007266</td>\n",
       "      <td>0.007703</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031352</td>\n",
       "      <td>0.028794</td>\n",
       "      <td>0.027069</td>\n",
       "      <td>0.026118</td>\n",
       "      <td>0.025420</td>\n",
       "      <td>0.025075</td>\n",
       "      <td>0.024451</td>\n",
       "      <td>0.023574</td>\n",
       "      <td>0.023398</td>\n",
       "      <td>0.023387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.366819</td>\n",
       "      <td>0.370616</td>\n",
       "      <td>0.374582</td>\n",
       "      <td>0.373159</td>\n",
       "      <td>0.372722</td>\n",
       "      <td>0.372896</td>\n",
       "      <td>0.372802</td>\n",
       "      <td>0.372324</td>\n",
       "      <td>0.373675</td>\n",
       "      <td>0.373736</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074927</td>\n",
       "      <td>0.074603</td>\n",
       "      <td>0.073396</td>\n",
       "      <td>0.071710</td>\n",
       "      <td>0.070799</td>\n",
       "      <td>0.069748</td>\n",
       "      <td>0.069445</td>\n",
       "      <td>0.069362</td>\n",
       "      <td>0.068663</td>\n",
       "      <td>0.067959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 262 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                                                    \\\n",
       "year             1880      1881      1882      1883      1884      1885   \n",
       "last_letter                                                               \n",
       "a            0.345587  0.343440  0.338764  0.341251  0.338550  0.341270   \n",
       "b                 NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "c                 NaN       NaN  0.000046  0.000045       NaN       NaN   \n",
       "d            0.006693  0.006601  0.006806  0.007211  0.007100  0.006478   \n",
       "e            0.366819  0.370616  0.374582  0.373159  0.372722  0.372896   \n",
       "\n",
       "sex                                                    ...            M  \\\n",
       "year             1886      1887      1888      1889    ...         2001   \n",
       "last_letter                                            ...                \n",
       "a            0.339703  0.335258  0.332764  0.328706    ...     0.020162   \n",
       "b                 NaN       NaN       NaN       NaN    ...     0.026256   \n",
       "c                 NaN       NaN       NaN       NaN    ...     0.013972   \n",
       "d            0.006967  0.007035  0.007266  0.007703    ...     0.031352   \n",
       "e            0.372802  0.372324  0.373675  0.373736    ...     0.074927   \n",
       "\n",
       "sex                                                                      \\\n",
       "year             2002      2003      2004      2005      2006      2007   \n",
       "last_letter                                                               \n",
       "a            0.020019  0.019177  0.019505  0.018481  0.017635  0.016747   \n",
       "b            0.025418  0.024368  0.023171  0.021645  0.020778  0.020357   \n",
       "c            0.014048  0.014042  0.013514  0.013083  0.012991  0.012983   \n",
       "d            0.028794  0.027069  0.026118  0.025420  0.025075  0.024451   \n",
       "e            0.074603  0.073396  0.071710  0.070799  0.069748  0.069445   \n",
       "\n",
       "sex                                        \n",
       "year             2008      2009      2010  \n",
       "last_letter                                \n",
       "a            0.016189  0.015927  0.014980  \n",
       "b            0.019655  0.019693  0.020470  \n",
       "c            0.012458  0.012186  0.012181  \n",
       "d            0.023574  0.023398  0.023387  \n",
       "e            0.069362  0.068663  0.067959  \n",
       "\n",
       "[5 rows x 262 columns]"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# table = top1000.pivot_table('prop', index='year',\n",
    "#                            columns='sex', aggfunc=sum)\n",
    "\n",
    "letter_prop = table / table.sum()\n",
    "letter_prop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>last_letter</th>\n",
       "      <th>d</th>\n",
       "      <th>n</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>0.083055</td>\n",
       "      <td>0.153213</td>\n",
       "      <td>0.075760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>0.083247</td>\n",
       "      <td>0.153214</td>\n",
       "      <td>0.077451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>0.085340</td>\n",
       "      <td>0.149560</td>\n",
       "      <td>0.077537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>0.084066</td>\n",
       "      <td>0.151646</td>\n",
       "      <td>0.079144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.086120</td>\n",
       "      <td>0.149915</td>\n",
       "      <td>0.080405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "last_letter         d         n         y\n",
       "year                                     \n",
       "1880         0.083055  0.153213  0.075760\n",
       "1881         0.083247  0.153214  0.077451\n",
       "1882         0.085340  0.149560  0.077537\n",
       "1883         0.084066  0.151646  0.079144\n",
       "1884         0.086120  0.149915  0.080405"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dny_ts = letter_prop.ix[['d', 'n', 'y'], 'M'].T\n",
    "dny_ts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有了这个时间序列的DataFrame后，就可以通过其plot方法绘制出一张趋势图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1172b3a90>"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2ZolDkoTncHRMm2/5GcHd3R0HBwfOnDnzzLmT6d1nIoQQIk3surKLtxe+Te2itfmz7Z9m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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11785c630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dny_ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 变成女孩名字的男孩名字（以及相反的情况）\n",
    "\n",
    "另一个有趣的趋势是，早年流行于男孩的名字近年来“变性了”，列入Lesley或Leslie。回到top1000数据集，找出其中以\"lesl\"开头的一组名字："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Leslie', 'Lesley', 'Leslee', 'Lesli', 'Lesly'], dtype=object)"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_names = top1000.name.unique()\n",
    "mask = np.array(['lesl' in x.lower() for x in all_names])\n",
    "lesley_like = all_names[mask]\n",
    "lesley_like"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后利用这个结果过滤其他的名字，并按名字分组计算出生数以查看相对频率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Leslee      1082\n",
       "Lesley     35022\n",
       "Lesli        929\n",
       "Leslie    370429\n",
       "Lesly      10067\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered = top1000[top1000.name.isin(lesley_like)]\n",
    "filtered.groupby('name').births.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来，我们按性别和年度进行聚合，并按年度进行规范化处理："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>8.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>11.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>9.0</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>7.0</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>15.0</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex      F      M\n",
       "year             \n",
       "1880   8.0   79.0\n",
       "1881  11.0   92.0\n",
       "1882   9.0  128.0\n",
       "1883   7.0  125.0\n",
       "1884  15.0  125.0"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = filtered.pivot_table('births', index='year',\n",
    "                             columns='sex', aggfunc='sum')\n",
    "\n",
    "table[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>0.091954</td>\n",
       "      <td>0.908046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>0.106796</td>\n",
       "      <td>0.893204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>0.065693</td>\n",
       "      <td>0.934307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>0.053030</td>\n",
       "      <td>0.946970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.107143</td>\n",
       "      <td>0.892857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex          F         M\n",
       "year                    \n",
       "1880  0.091954  0.908046\n",
       "1881  0.106796  0.893204\n",
       "1882  0.065693  0.934307\n",
       "1883  0.053030  0.946970\n",
       "1884  0.107143  0.892857"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = table.div(table.sum(1), axis=0)\n",
    "table[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex     F   M\n",
       "year         \n",
       "2006  1.0 NaN\n",
       "2007  1.0 NaN\n",
       "2008  1.0 NaN\n",
       "2009  1.0 NaN\n",
       "2010  1.0 NaN"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，我们可以轻松绘制一张分性别的年度曲线图了："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117ee2e10>"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PP/+UlJQUuXTpklhbW8vkyZNNkJQsxZAhQzKtpZImNTW1QKfikXbMogBIu2ZesWJFee+9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117c8e518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.plot(style={'M': 'k-', 'F': 'k--'})"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [py35]",
   "language": "python",
   "name": "Python [py35]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
